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Aimuamwonsa Osahenoto Monebi and Osarobo Osamede Ogbiede. "Data-driven decision support methodology for enhancing production machine availability." World Journal of Advanced Research and Reviews 25, no. 2 (2025): 1858–72. https://doi.org/10.30574/wjarr.2025.25.2.0547.
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Unscheduled preventive maintenance negatively impacts product quality and increases production time due to downtime and emergency shutdowns, raising production costs. We propose a decision support methodology to enhance equipment availability by analyzing historical time to repair (TTR) data using statistical analysis in Minitab. This study analyzed TTR data from seven machines (Filler, Mixer, Blowmould, Labeller, Variopac, Palletizer, and Conveyor) on a production line for 2022. The analysis included both parametric and non-parametric methods, with results presented graphically to summarize statistics like cumulative repair time probability (CRTPR1) and the hazard rate. Using least squares probability fitting, we found that five machines followed an exponential distribution, while the Palletizer and Mixer exhibited log-normal distributions. All machines had about a 63% probability of completing repairs within the meantime to repair (MTTR), except the Palletizer and Mixer, which showed less than 1% probability.
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Teymourian, Kiumars, Phillip Tretten, Dammika Seneviratne, and Diego Galar. "Ergonomics Evaluation in Designed Maintainability: Case Study Using 3 DSSPP." Management Systems in Production Engineering 29, no. 4 (2021): 309–19. http://dx.doi.org/10.2478/mspe-2021-0039.
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Abstract Maintainability is one of the design parameters (reliability, availability, maintainability, and safety (RAMS)) and maintenance is needed to keep the respective design in sustainable use. At the same time, the human is involved in the form of interface and interaction in an engineered product/system designed. Ergonomics is a multi-disciplinary science that considers human capabilities and limitations in a broader sense. The objective of this paper is to integrate ergonomics into the maintainability design process in order to facilitate maintenance operation in lesser; time, cost, easier operation as well as the well-being of human who is involved. In other words, good ergonomics lead to good economics and in a broader sense, sustainability. This investigation shows that designing comfortable workplaces and lesser workload for maintenance operators will be beneficial for the maintainability design process and also improve the meantime to repair MTTR. In order to evaluate the effect of designed work-place and workload on maintainers 3 D Static Strength Prediction Program (3D SSPP) that is commonly used as an ergonomics evaluation tool in scientific studies was applied.
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Utomo, Rezza Wira. "Perencanaan Perawatan Mesin Pump 107 Dengan Metode Reability Centered Maintenance (RCM) di PT. Petrokimia Gresik." Jurnal Energi dan Teknologi Manufaktur (JETM) 1, no. 02 (2018): 33–38. http://dx.doi.org/10.33795/jetm.v1i02.13.
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PT. Petrokimia Gresik is one of the largest fertilizer companies in Indonesia, locatedin Gresik, East Java. The pump 107 engine on the Ammonia unit is often damaged due to itslargest size compared to other types of pumps, as well as the result of carrying thick orconcentrated fluid (bluish black) so that it is heavy for the drainage process. The purpose ofthis study is to plan and recommend the proposed treatment method on the pump enginebased on the method used to improve the working efficiency of the pumping machine 107and determine the failure mode and diagnosis of the effects of failure modes that occur in thecomponent. The method used is the Reability Centered Maintenance (RCM) method whichis expected to be able to produce maintenance or maintenance scheduling that is increasinglydirected so that it can improve the performance and efficiency of the engine, reduce repaircosts, and extend the service life of the machine itself. From this study, the results are in theform of Faliure Model And Effect Analysis (FMEA), FMEA table preparation is carried outbased on component function data and maintenance reports which can then be determined byvarious failures resulting in malfunction. From the compilation of FMEA, it can be seenwhat the causes of failure are and what impacts they have caused. Next, the value of MeanTime Between Failure (MTBF) pump 107-JA is 15,829 hours, pump 107-JB is 43,764 hoursand pump 107-JCM is 19,578 hours. Maintainability M (t) or Mean Time to Repair (MTTR)value on pump 107-JA is 2,914 hours, pump 107-JB is 3,411 hours, and pump 107-JCM is3,1 hours, Availability A (t) value is pump 107-JA at 84.44%, pump 107-JB at 92.76% andpump 107-JCM at 86.31%. The last one is found that the failure rate of pump 107-JA is0.063172, pump 107-JB is 0.02284 and pump 107-JCM is 0.051.
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Makahanap, Malvin Edward. "<i>Availibilty </i>Pada Aplikasi <i>mobile Banking: Case Study</i> Bank XYZ." Jurnal Teknologi Informasi dan Ilmu Komputer 11, no. 1 (2024): 191–98. http://dx.doi.org/10.25126/jtiik.20241117848.
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Saat ini, perkembangan teknologi internet dan fintech mempengaruhi kebiasaan sehari-hari dari pelanggan yang berakibat kepada pengguna teknologi untuk meningkatkan ekspektasi terhadap teknologi sistem perbankan online. Pelanggan berkespektasi terhadap Perusahaan penyedia layanan finansial untuk bisa mengizinkan pelanggan untuk memiliki akses terhadap layanan finansial setiap saat dan setiap waktu melalui perangkat milik mereka. Perusahaan finansial patut menyajikan pelanggan dengan layanan finansial melalui teknologi secara kontinu. Mobile Banking diharapkan untuk memiliki high-availability yang bisa menjaga layanan tetap mampu untuk beroperasi 24x7x365. Ekspektasi ini membuat availability menjadi salah satu fungsi kunci dalam bersaing dengan penyedia layanan Mobile Banking lain. Bank XYZ ingin meningkatkan fitur ini untuk memberikan pelanggan layanan perbankan dan pengalaman yang lebih baik kepada pelanggannya. Studi ini dilakukan untuk mengevaluasi availability dari layanan Mobile Banking milik Bank XYZ dengan tujuan untuk digunakan sebagai landasan dalam membuat peta peningkatan jangka panjang dan rencana mitigasi jangka pendek agar tidak tertinggal dibelakang kompetitor pada saat ini. Evaluasi availability dilakukan dengan melakukan kalkulasi atas waktu yang dibutuhkan oleh komponen yang bisa diperbaiki untuk pulih dari kondisi unavailable pada periode waktu tertentu. MTTR (Mean Time to Repair) dan MTBF (MeanTime Between Failure) digunakan dalam melakukan analisa terhadap availability. Evaluasi dilakukan terhadap ketersediaan sistem mobile banking dan ketersediaan fungsi yang disediakan mobile banking. Hasil evaluasi kemudian di selaraskan dengan Availability class untuk mengetahui lebih lanjut tingkatan availability saat ini dari sistem dan fungsionalitas. Pareto Analysis dilakukan untuk mengklasifikasikan dan memperingkatkan penyebab dari downtime yang terjadi pada sistem. Berdasarkan hasil dari analisa, bisa diperjelas kondisi saat ini dari availability layanan Mobile Banking yang bisa dijadikan landasan dalam menentukan strategi pengembangan. Berdasarkan penelitian yang dilakukan pada sistem mobile banking bank XYZ, diperoleh hasil availability secara keseluruhan sebesar 98,34% tergolong “Class 1 –Unmanaged”. Untuk layanan yang paling sering digunakan dengan tingkat ketersediaan tertinggi adalah layanan “Cek Saldo” dengan availability sebesar 98,34% dan terendah adalah “Pembelian Token Listrik” dengan availability sebesar 97,56%. Unavailability selama periode penelitian terjadi karena aktivitas product development, aktivitas terkait security, production issue, hardware issue dan 3rd party maintenance. Berdasarkan analisis Pareto, aktivitas product development dan aktivitas terkait security merupakan isu paling kritis yang perlu diprioritaskan terlebih dahulu untuk mitigasi jangka pendek maupun solusi jangka panjang.
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Novarika, Wirda, Mahrani Arfah, and Ridho Agustian. "Analisis Preventive Maintenance pada Mesin Heater Kernel Dengan Metode Menghitung Mean Time Between Failure (MTBF) dan Mean Time To Repair (MTTR) di PT. Supra Matra Abadi." JURNAL UNITEK 16, no. 2 (2023): 259–67. http://dx.doi.org/10.52072/unitek.v16i2.544.
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Tujuan dari penelitian ini untuk meminimalisir dan mengetahui jadwal Preventive Maintenance mesin Heater Kernel yang efektif dan mengetahui sejauh mana Preventive Maintenance pada mesin Heater Kernel dengan metode Mean Time Between Failure (MTBF) dan Mean Time To Repair (MTTR) dapat membantu mengurangi Breakdown dan downtime. Mean Time Between Failure (MTBF) dan Mean Time to Repair (MTTR) adalah salah satu metode sebagai acuan untuk menetapkan jadwal perawatan yang efektif. Oleh karena itu perlu tindakan preventive maintenance agar dapat meningkatkan kinerja dari perusahaan, hasil analisa didapatkan nilai Mean Time Between Failure (MTBF) 18.830 menit, yang mana hasil perhitungan ini didapat dari menghitung waktu mesin selesai diperbaiki sampai mesin mengalami kerusakan kembali dan Mean Time to Repair (MTTR) 257 menit, yang dihasilkan dari perhitungan mekanik mulai memperbaiki mesin sampai mekanik selesai memerbaiki mesin. Hasil penerapan tindakan preventive maintenance rata-rata 79,80% sehingga mesin mampu bekerja secara optimal
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R.O., Edokpia and O.C. Onuigbo. "Maintainability Assessment Of 11kv Feeders In Benin City, Edo State, Nigeria." Journal of Energy Technology and Environment 5, no. 2 (2023): 25–31. https://doi.org/10.5281/zenodo.8018191.
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<em>Nigeria’s power supply has been experiencing incessant interruptions due to failures in the distribution system. The maintainability of the power system is important in meeting customers demand. The maintainability of three 11kv feeder in Benin Electricity Distribution Company (BEDC), Edo State Nigeria is evaluated in this study. the failure data which includes; time of failure, time outage was restored, causes of failure, and the outage duration(also known as ‘repair time’) from the Injection substations of the three feeders for the year, 2020 and 2021 was collated and used . Monthly and Yearly Mean time to repair (MTTR) and repair rate were calculated for the analysis. The analysis results revealed that the year 2021 had the accumulated higher MTTR than 2020 for the feeders as a result of decline in response to faults except in the Ihama Feeder that had a decrease in MTTR for the year 2021 which implies that there was improvement in response to faults</em>
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Li, Yong Xiang, Ying Li, and Chuan Lv. "An Application of Time Classification in the System Maintenance Allocation." Applied Mechanics and Materials 251 (December 2012): 91–96. http://dx.doi.org/10.4028/www.scientific.net/amm.251.91.
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The aim of the maintenance allocation is to allocate the maintenance time of the subsystems reasonably and accurately. Traditional MTTR (mean time to repair) allocation methods are most often based on the products’ complexity. However, there are a lot of deficiencies in practical application. To allocate the system MTTR of the subsystems more precise and reasonable, a better model is needed. The aim of this paper is to develop an improved model to allocate the maintenance time, as well as picke out the common maintenance time and allocating e the MTTR in the subsystems more accurately.
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Lv, Chuan, and Jian Sen Yang. "An Application of the Common Maintenance Time in the System Maintenance Allocation." Applied Mechanics and Materials 470 (December 2013): 703–6. http://dx.doi.org/10.4028/www.scientific.net/amm.470.703.
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The maintenance allocation methods play an important role in the design and maintenance of product. However, lots of deficiencies exist in practical application by using these methods. To allocate the system MTTR( Mean Time to Repair) to the subsystems more precisely and reasonable, a better model is needed. The purpose of this article is to raise an improved method to allocate the MTTR more accurately.
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Watupongoh, Richard V. B., and Dedy Rusmiyanto. "Analisis Perawatan Alat Bongkar Muat Rubber Tyred Gantry di Terminal Peti Kemas Semarang (TPKS)." Ocean Engineering : Jurnal Ilmu Teknik dan Teknologi Maritim 3, no. 4 (2024): 101–10. https://doi.org/10.58192/ocean.v3i4.3046.
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This study aims to analyze the reliability and performance of Rubber Tyred Gantry (RTG) cranes using the Mean Time To Repair (MTTR), Mean Time Between Failures (MTBF), and Inherent Availability (IA) methods. The MTTR method is used to measure the average time required for repairs, while MTBF calculates the average time between failures. Meanwhile, the IA method assesses the inherent availability of the equipment based on the previous two parameters. The results indicate that some RTG units experience high MTTR, such as RTG035-12, which recorded a repair time of 124.15 hours in January, and RTG035-13 with 441.23 hours in February. On the other hand, RTG035-19 demonstrated more stable performance with low MTTR in several months. In terms of MTBF, RTG035-16 recorded the lowest value of 0 hours in July, indicating significant disruptions, whereas RTG035-12 exhibited the best performance with the highest MTBF of 614 hours in December. The IA analysis revealed that some RTGs had low values, such as RTG035-13, which only reached 0.312 in February, while RTG035-19 maintained values above 0.95 throughout the year, demonstrating high reliability. This study provides insights into the performance of RTG equipment at the Semarang Container Terminal (TPKS) and emphasizes the need for effective maintenance strategies to enhance operational reliability and efficiency.
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Hia, Selamat W., and Prasetyo Adji Dian Pramudjito. "Reduce mean time to repair of mining equipment with lean six sigma." Operations Excellence: Journal of Applied Industrial Engineering 16, no. 3 (2024): 331. https://doi.org/10.22441/oe.2024.v16.i3.125.
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As the size and complexity of mining equipment grow, the impact of equipment failure becomes increasingly critical, often leading to costly downtime and production delays. Mining equipment performance depends on several factors, including equipment reliability, the operating environment, maintenance efficiency, operating processes, and the technical expertise of the personnel. Improving equipment reliability is essential to address these challenges, and one effective approach is to reduce the mean time to repair (MTTR), which significantly affects equipment availability. This study presents a case study on reducing the MTTR of the starter motor for coal mining equipment using the Lean Six Sigma method. Lean Six Sigma combines the principles of lean manufacturing and Six Sigma to identify inefficiencies and reduce variability. The findings reveal a substantial reduction in MTTR from 110 minutes to 10 minutes. Statistical analysis using a two-sample t-test confirms the significance of the improvement, with the process capability index (cpk) increasing from -2.94 to 6.96. This case study demonstrates the potential of Lean Six Sigma to address critical maintenance challenges, improve operational efficiency, and enhance equipment reliability in the mining sector.
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Muhammad Ridzuan, Mohd, and Sasa Djokic. "Energy Regulator Supply Restoration Time." Energies 12, no. 6 (2019): 1051. http://dx.doi.org/10.3390/en12061051.
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In conventional reliability analysis, the duration of interruptions relied on the input parameter of mean time to repair (MTTR) values in the network components. For certain criteria without network automation, reconfiguration functionalities and/or energy regulator requirements to protect customers from long excessive duration of interruptions, the use of MTTR input seems reasonable. Since modern distribution networks are shifting towards smart grid, some factors must be considered in the reliability assessment process. For networks that apply reconfiguration functionalities and/or network automation, the duration of interruptions experienced by a customer due to faulty network components should be addressed with an automation switch or manual action time that does not exceed the regulator supply restoration time. Hence, this paper introduces a comprehensive methodology of substituting MTTR with maximum action time required to replace/repair a network component and to restore customer duration of interruption with maximum network reconfiguration time based on energy regulator supply requirements. The Monte Carlo simulation (MCS) technique was applied to medium voltage (MV) suburban networks to estimate system-related reliability indices. In this analysis, the purposed method substitutes all MTTR values with time to supply (TTS), which correspond with the UK Guaranteed Standard of Performance (GSP-UK), by the condition of the MTTR value being higher than TTS value. It is nearly impossible for all components to have a quick repairing time, only components on the main feeder were selected for time substitution. Various scenarios were analysed, and the outcomes reflected the applicability of reconfiguration and the replace/repair time of network component. Theoretically, the network reconfiguration (option 1) and component replacement (option 2) with the same amount of repair time should produce exactly the same outputs. However, in simulation, these two options yield different outputs in terms of number and duration of interruptions. Each scenario has its advantages and disadvantages, in which the distribution network operators (DNOs) were selected based on their operating conditions and requirements. The regulator reliability-based network operation is more applicable than power loss-based network operation in counties that employed energy regulator requirements (e.g., GSP-UK) or areas with many factories that required a reliable continuous supply.
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Okereke, Nnaemeka Francis, and Godson Chijioke Akaninwor. "Measurement of Performance Effectiveness of A Palm-Nut Oil Extraction Line Using Maintenance Metrics." Research and Reviews on Experimental and Applied Mechanics 7, no. 3 (2024): 30–33. https://doi.org/10.5281/zenodo.14273460.
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<em>The economic enhancement in Palm-nut oil extraction line was the major drive of the undertaken project. This study is to measure the performance effectiveness of palm-nut oil extraction line using maintenance metrics like Mean-Time-To-Repair MTTR and Mean-Time-Before-Failures MTBF. The process adopted was to measure MTTR and MTBF of the equipment in the Palm-nut Oil extraction line to determine their effectiveness. The MTTR and MTBF of palm-nut washer, palm-nut steamer, and palm-nut extractor were obtained with their maximum rate, efficiency and throughput. Then, the entire efficiency of the extraction line is evaluated as 87.1812%. while the throughput of the entire palm-nut extraction line is 3.21839 kg/min. The effectiveness of the equipment is that the entire line processes 3.21839 kg of palm-nut in every minute for the run time of 420 minutes. In conclusion, the efficiency is higher, but the MTTR will be reduced further and the MTBF increased for higher efficiency and reliability of the palm-nut oil extraction equipment.</em>
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Uli, Hotma, Muhammad Rizky Ikhsan, and Setyo Yulio Pratama. "The Reliability Analysis of Rotary Kiln A." International Journal of Education, Science, Technology, and Engineering 3, no. 2 (2020): 67–77. http://dx.doi.org/10.36079/lamintang.ijeste-0302.155.
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Rotary Kiln as a pyroprocessing device is used to raise the temperature of the material to a high temperature (calcination) in a continuous process. By looking at the constraints on PT. Meratus Jaya Iron & Steel, whose production machines often have breakdowns, is a fundamental reason for researchers to increase the reliability level of PT. Meratus Jaya Iron & Steel. The research was conducted on the Rotary Kiln Machine made in Germany which plays a high enough role in the company. The method used is the Reliability with Mean Time to Repair Method (MTTR). From the results of calculations of Reliability with MTTR on Rotary Kiln A is Reliability at the time of 296 hours operation results obtained by 0.5371 and MTTR 537.7732. The higher the reliability value, the better the machine used in the operation process.
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Uli, Hotma, Muhammad Rizky Ikhsan, and Setyo Yulio Pratama. "The Reliability Analysis of Rotary Kiln A." International Journal of Education, Science, Technology, and Engineering 4, no. 1 (2021): 13–23. http://dx.doi.org/10.36079/lamintang.ijeste-0401.155.
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Rotary Kiln as a pyroprocessing device is used to raise the temperature of the material to a high temperature (calcination) in a continuous process. By looking at the constraints on PT. Meratus Jaya Iron & Steel, whose production machines often have breakdowns, is a fundamental reason for researchers to increase the reliability level of PT. Meratus Jaya Iron & Steel. The research was conducted on the Rotary Kiln Machine made in Germany which plays a high enough role in the company. The method used is the Reliability with Mean Time to Repair Method (MTTR). From the results of calculations of Reliability with MTTR on Rotary Kiln A is Reliability at the time of 296 hours operation results obtained by 0.5371 and MTTR 537.7732. The higher the reliability value, the better the machine used in the operation process.
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Tampoy, Divtara, Priskila Rully Setiyaningrum, and Muhammad Kadarisman. "ANALYSIS OF BLOWOUT PREVENTER MAINTENANCE PERFORMANCE ON RIG #55 AND RIG #99 BASED ON DEGRADATION TEST DATA IN "DERE" FIELD." PETRO: Jurnal Ilmiah Teknik Perminyakan 13, no. 2 (2024): 73–85. https://doi.org/10.25105/petro.v13i2.19826.
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Blowout Preventer (BOP) digunakan untuk mengatasi risiko semburan liar dengan menutup sumur sebelum terjadinya semburan. Penelitian ini akan menganalisis masalah yang terjadi pada BOP di Rig #55 dan Rig #99 di Lapangan “DERE” menggunakan metode performance maintenance. Data yang digunakan untuk menganalisis kinerja BOP di kedua Rig tersebut adalah data degradation test. Metode performance maintenance akan menghitung MTBF (Mean Time Between Failures), MTTR (Mean Time To Repair), serta availability. Selain itu, akan dilihat penyebab penurunan kinerja dan memberikan rekomendasi optimalisasi perawatan BOP dengan menggunakan diagram fishbone. Hasil analisis menunjukkan bahwa selama beroperasi pada periode tahun 2023 Rig #55, nilai MTBF adalah 11520 menit, MTTR adalah 2160 menit, availability 89%. Rig #99, nilai MTBF adalah 18.720 menit, MTTR adalah 1440 menit dan availability 92%. Sedangkan Rig #99 di tahun 2021 nilai MTBF adalah 40320 menit, MTTR adalah 1440 menit, availability 96%. Faktor kemunduran yang terjadi pada Rig #55 dan Rig #99 dipengaruhi oleh packing element, sehingga perlu dilakukan pengecekan dan penggantian sesuai dengan jam operasionalnya. Aspek lingkungan tempat kerja, kurangnya kebersihan area BOP, usia part yang sudah usia, prosedur pengantian part belum berjalan efektif dan faktor manusia kurang konsentrasi dan kurangnya kepedulian terhadap SOP menyebabkan penurunan kualitas pada BOP.
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Permatasari, Chintya, Ni Nyoman Madhawi Dewi Dasi, and Muhammad Wildan. "ANALISIS METODE MAINTENANCE BERDASARKAN MTBF DAN MTTR PADA PERALATAN NAVIGASI DI PERUM LPPNPI CABANG DENPASAR." Jurnal Review Pendidikan dan Pengajaran 8, no. 1 (2025): 1078–84. https://doi.org/10.31004/jrpp.v8i1.40701.
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Peralatan CNSD (Communication, Navigation, Surveillance, dan Data Processing) pada Perusahaan Umum Lembaga Penyelenggara Pelayanan Navigasi Penerbangan Indonesia (LPPNPI) cabang Denpasar dalam mengurangi resiko kerusakan pada peralatan, perawatan ini juga bertujuan untuk memperpanjang umur peralatan,meningkatkan kinerja dan efisiensi, dan pada akhirnya mengurangi biaya dan operasional secara keseluruhan. Tujuan kegiatan ini adalah untun menganalisis metode maintenance berdasarkan Mean Time Between Failure (MTBF) dan Mean Time To Repair (MTTR) pada peralatan CNSD. Berdasarkan peraturan KP 35 Tahun 2019 terkait penerapan metode maintenance yang efektif untuk menjaga keseimbangan antara MTBF yang tinggi dan MTTR yang rendah untuk meningkatkan efisiensi dan keselamatan operasional pada peralatan CNSD.
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Fatma, Nur Fadilah, Henri Ponda, and Rizky Aditya Kuswara. "ANALISIS PREVENTIVE MAINTENANCE DENGAN METODE MENGHITUNG MEAN TIME BETWEEN FAILURE (MTBF) DAN MEAN TIME TO REPAIR (MTTR) (STUDI KASUS PT. GAJAH TUNGGAL TBK)." Heuristic 17, no. 2 (2020): 87–94. http://dx.doi.org/10.30996/heuristic.v17i2.4648.
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Mesin beroperasi secara terus menerus menyebabkan menurunnya tingkat kehandalan peralatan serta menyebabkan sering terjadinya breakdown dan downtime yang tinggi pada mesin-mesinnya terutama pada mesin Extruder (ITE). Untuk meminimalisir terjadinya breakdown dan downtime maka perlu adanya sistem penjadwalan perawatan yang baik guna mencegah terjadinya kerusakan mesin. Mean Time Between Failure (MTBF) dan Mean Time to Repair (MTTR) adalah salah satu metode sebagai acuan untuk menetapkan jadwal perawatan yang efektif. Oleh karena itu, diperlukannya tindakan preventive maintenance agar dapat meningkatkan kinerja dari perusahaan, Dari hasil analisa didapatkan nilai Mean Time Between Failure (MTBF) 259,04 menit dan Mean Time to Repair (MTTR) 19.990,1 menit. Perubahan penjadwalan preventive maintenance dapat dilakukan dengan interval waktu 2 minggu sekali untuk aktivitas cleaning ac panel dan cleaning motor blower. Hasil penerapan tindakan preventive maintenance rata-rata 98% sehingga mesin mampu bekerja secara optimal.
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Aji, J. O., and I. Uchendu. "Improving Facility Operations: A Quantitative Evaluation of MTBF, MTTR, and SLA Targets." European Journal of Innovative Studies and Sustainability 1, no. 3 (2025): 247–61. https://doi.org/10.59324/ejiss.2025.1(3).20.
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This study evaluated facility operations services by means of exploring key metrics like Mean Time to Repair (MTTR), Mean Time Between Failures (MTBF), Service Level Agreement (SLA) targets, Reliability (R(t)) and Failure Rate (λ). The analysis was carried out in three regions namely Eastern region, Northern region, and Western region by x-raying 10 months of data collected from operational records, incident logs, and SLA compliance results from a facilities maintenance company in Nigeria. The Western region was faced with the highest number of incidents, peaking at 933 in the month of April, while that of the Eastern region recorded 371 incidents in the month of May, and the Northern region had 166 incidents in the month of August all in year 2024. MTBF was used to measure failure frequency, while the MTTR provided insights into incident resolution times. In the Western region, the MTTR averaged 2.5 hours in in the month of April, resulting to operational inefficiencies. The facility efficiency was measured against a 95% SLA target and the performance shows a drop to about 76% in the month of March but there was efforts along the line that shows recovery to 100% by the month of September and October 2024. The reliability analysis also showed that the high incident rates negatively impacted operational continuity. The outcome of this study emphasizes the significance of constantly monitoring the MTBF, the MTTR, and the SLA compliance to improve operational efficiency. The findings also highlight the Western region’s operational challenges and the need for focused on improvements in the maintenance and repair processes method of pattern to attain steady 99-100% efficiency across all regions.
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Nurdiansyah, Dida, and Agustinus Hariadi D.P. "Analysis of Maintenance Management at PT.XYZ Power Plant (With MTTR, MTTF, and Availability Factors) and Development of Performance Improvement Program." Formosa Journal of Multidisciplinary Research 3, no. 9 (2024): 3363–76. http://dx.doi.org/10.55927/fjmr.v3i9.11137.
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Damage data for machinery at PT. XYZ power plant between October-December 2023 shows that the pump is the component with the highest number of failures, recording 28 incidents (33.7% of the total 83 failures), followed by the condenser with 27 incidents (32.5%). The combined failures of these two components account for 66.3% of the total failures, indicating a need for focused improvements on the pump and condenser. The MTTR analysis for the pump shows a high average repair time: 7.7 hours in October, 7 hours in November, and 7.7 hours in December, with low availability rates (89.64%-89.69%). The MTTF for the pump varies from 65.2 to 75.4 hours, indicating the need for improved preventive maintenance strategies. It is recommended that maintenance be carried out every 2-3 days or at least once a week. The condenser has an MTTR ranging from 6.7 to 7.7 hours and low availability (88.89%-89.95%), with MTTF varying from 66.7 to 92.7 hours. The high MTTR and low availability of these two components highlight the need for improvements in maintenance management to enhance performance.
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O.C.Onuigbo, and R.O. Edokpia. "Availability Assessment of Three 11kv Feeders Benin City, Edo State, Nigeria." Journal of Energy Technology and Environment 5, no. 2 (2023): 32–39. https://doi.org/10.5281/zenodo.8018218.
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<em>Nigeria’s power supply has been experiencing incessant interruptions due to failures in the distribution system. The availability assessment of power system is a necessary criterion in meeting the customer’s demand. In this study, the availability of three 11kv feeder in Benin Electricity Distribution Company (BEDC), Edo State Nigeria were evaluated. These was carried out by collating the failure data which includes; time of failure, time outage was restored, outage duration, causes of failure, and load interruption from the Injection substations G.R.A Injection substations for the years, 2020 and 2021 using Monthly and Yearly data. Mean time between failure (MTBF), Mean time to repair (MTTR), failure rate, repair rate, availability evaluated for the analysis. The results of the analysis reveals that there was a decrease for MTBF for the feeders (which implies decline in Performance). The year 2021 had the accumulated higher MTTR than 2020 as a result of decline in response to faults except for Ihama Feeder. After the computations, the year 2020 had a better availability than the year2021</em>
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Silvia, Silvia, Reviana Inda Dwi Suyatmo, and Murnianti Murnianti. "Analisis Preventive Maintenance Berdasarkan Mean Time Between Failure (MTBF) Dan Mean Time To Repair (MTTR) Pada Alat Blow Molding Di PT XYZ." Jurnal Pengabdian Masyarakat Bangsa 2, no. 8 (2024): 3471–78. http://dx.doi.org/10.59837/jpmba.v2i8.1495.
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PT XYZ merupakan perusahaan yang bergerak di bidang manufaktur aksesoris mobil otomotif dan interior kelautan yang berkembang. Sebagian besar produk plastik yang dihasilkan dibuat menggunakan mesin blow molding dengan produk yang sangat bervariasi Sehingga, apabila terjadi kerusakan maka akan menyebabkan produksi tidak bisa berjalan sebagaimana semestinya. Tujuan kegiatan ini adalah menganalisis Preventive Maintenance berdasarkan Mean Time Between Failure (MTBF) dan Mean Time To Repair (MTTR) pada alat blow molding. Preventive maintenance merupakan bagian dari Planned Maintenance Pillar yang merupakan salah satu pillar dalam Total Productive Maintenance (TPM). Metode ini merupakan bagian utama dari sistem pemeliharaan yang banyak diterapkan oleh perusahaaan Jepang dan memiliki peranan penting, karena dapat menekan biaya produksi, meningkatkan produktivitas, dan efisiensi mesin/peralatan sehingga kerugian yang diakibatkan oleh kerusakan mesin dapat dihindarkan. Berdasarkan data yang diperoleh, nilai MTBF mesin blow 2 dan 3 lebih rendah daripada mesin blow molding 1 dan 4 sedangkan nilai MTTR sebesar 104,5 menit. Total failure pada mesin blow molding 1, 2, 3, dan 4, failure pada jet loader dan pressure paling sering terjadi. Untuk penentuan kebijakan preventive maintenance dilakukan dengan meminimalkan biaya dilakukan. Rekomendasi yang dapat dilakukan adalah pelaporan waktu perbaikan tiap terjadi kerusakan dilengkapi, estimasi biaya untuk menghitung jangka waktu preventive maintenance, serta perhitungan MTBF MTTR untuk mesin yang lain.
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Abdul Wahid Arohman, Muhamad Agus, Solahhudin, and Desy Agustin. "Analisis Preventive Maintenance pada Mesin Injection Molding dengan Metode Mean Time Between Failure dan Mean Time to Repair di PT. XZY." Jurnal Serambi Engineering 9, no. 1 (2023): 7623–30. http://dx.doi.org/10.32672/jse.v9i1.720.
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A series of predictable preventive activities to overcome machine failure during operation is very important because it produces good and stable spare parts. This research was carried out with the aim of preventing machine damage due to short circuits that occur in injection molding machines used for the sudden production of automotive component spare parts. The research was carried out using the Mean Time Between Failure (MTBF) and Mean Time To Repair (MTTR) approaches to calculate the time between the machine breaking down and the time it was repaired. The MTBF calculation results in this research were 72.9 hours or less than 4 days the engine would experience damage or every 72.9 hours the engine would experience damage again. The MTTR value obtained is 2 hours, where in 2 hours the work to overcome the damage can be done.
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Jeang, Angus, Chang Pu Ko, Chien-Ping Chung, You-Jie Chen, and I. Lin. "Optimal availability for determining choice and repair policy of system components." International Journal of Quality & Reliability Management 36, no. 3 (2019): 347–57. http://dx.doi.org/10.1108/ijqrm-12-2017-0255.
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Purpose The purpose of this paper is to establish the regression model by a simulation method that was obtained by using the system response at unit cost as the response value. The unit availability was maximized, while the unit cost was minimized. Design/methodology/approach In this study, the Monte Carlo simulation method was used to simulate an operational system, and the regression model was obtained by using response surface methodology with the experimental matrix and different levels of experimental combinations. Findings The optimal value of mean time between failure (MTBF) and mean time to repair (MTTR) of each component was then obtained by using the system response at unit cost as the response value. Practical implications Due to the upgrading of industrial technology and the maturity of electronic technology, product development technology has become highly sophisticated with complex designs. Reliability engineering has become a key procedure of high-tech industry. Social implications Based on the system availability of unit cost as the response value, it can maximize the availability to help decision makers to formulate the best selection strategy components and repair strategy. Originality/value Previous works regarding the parameter settings of reliability values never mention the simulation methodology. However, this study aims to achieve the above goals in finding the relationship of MTBF and MTTR simultaneously.
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Hariyanto, Arik dwi, and Dwi Iryaning Handayani. "TOTAL PRODUCTIVE MAINTENANCE PADA CHIPER AREA (Study Kasus di PT. Kutai Timber Indonesia Particle Board)." WAKTU: Jurnal Teknik UNIPA 13, no. 2 (2016): 1–8. http://dx.doi.org/10.36456/waktu.v13i2.57.
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Mesin merupakan komponen utama dalam proses produksi, apabila salah satu mesin mengalami kerusakan maka proses produksi akan berpengaruh, target produksi berkurang, dana untuk perbaikan kerusakan tinggi dan pada akhirnya perusahaan mengalami kerugian. Oleh karena itu perlu dilakukan perawatan mesin secara berkelanjutan agar kerusakan mesin dapat diminimalkan dan fasilitas produksi dapat bekerja sebagaimana yang diharapkan. Penelitian ini bertujuan dapat mengetahui performance maintenance yang diterapkan di PT. Kutai Timber Indonesia Particle Board. Penelitian ini menggunakan konsep Total Productive Maintenance dalam menganalisa terjadinya breakdown mesin pada Chiper Area. Nilai Mean Time Between Failure (MTBF) semakin meningkat sejumlah 112.83 menit sehingga peningkatan keandalannya dikatakan baik sedangkan nilai Mean Time To Repair (MTTR) sebesar 13,88 menit hal ini menunjukkan bahwa kemampuan operator maintenance kurang baik. Nilai Availability mesin mengalami peningkatan sebesar 89%. Dengan demikian perlu dilakukan training skill kepada operator maintenance, dikarenakan hasil MTTR yang didapatkan masih tidak stabil,selain itu
 perlu menerapkan perawatan mandiri atau small repair pada setiap mesin yang di operasikan, Availability mesin perlu ditingkatkan lagi, dengan nilai availability mesin yang lebih tinggi sehingga meningkatkan produktivitas tanpa mengesampingkan faktor-faktor yang lain. 
 
 Kata Kunci: Maintenance, Mesin, Produktivitas.
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Rohit Laheri. "Self-Healing Infrastructure: Leveraging Reinforcement Learning for Autonomous Cloud Recovery and Enhanced Resilience." Journal of Information Systems Engineering and Management 10, no. 49s (2025): 352–57. https://doi.org/10.52783/jisem.v10i49s.9888.
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Maintaining high availability and reliability in dynamic cloud environments demands proactive, automated solutions capable of handling failures at scale. This study introduces a novel multi-layer self-healing infrastructure framework that unites predictive analytics, reinforcement learning (RL), and rule-based automation into a cohesive, horizontally scalable system. Predictive analytics continuously ingests telemetry—CPU, memory, network metrics, and application logs—using time-series forecasting (ARIMA, LSTM) and unsupervised anomaly detection (Isolation Forest, k-Means) to flag potential faults with >96% accuracy. An RL agent employing Proximal Policy Optimization (PPO) then dynamically selects recovery actions (e.g., container restart, horizontal scaling, resource reallocation) guided by a reward function that balances rapid Mean Time To Repair (MTTR) reduction with minimal resource overhead and service impact. Simultaneously, rule-based playbooks address frequent failure patterns, ensuring immediate remediation within 30 seconds for predictable incidents. Deployed as Infrastructure as Code (IaC) via Terraform and Helm on Kubernetes clusters across AWS and Azure, our framework was validated over 220 fault scenarios. Key performance indicators demonstrate an 85% MTTR reduction (from 90 to 13.5 minutes), recovery reliability exceeding 95%, fault tolerance above 91%, and system uptime surpassing 98%. Resource overhead during recovery remains under 10%. Compared to prior isolated methods—rule-based MTTR reduction of 60%, RL-only MTTR reduction of 50%, and anomaly detection without remediation—our integrated model delivers superior resilience and operational efficiency. This paper is organized as follows: Section 1 introduces the problem and contributions; Section 2 reviews related work; Section 3 details the methodology; Section 4 describes experimental setup; Section 5 presents results; Section 6 discusses implications and limitations; Section 7 outlines future research directions; and Section 8 concludes.
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Lelu, Maria Eva Susanti, and Widya Setiafindari. "IMPLEMENTASI METODE RELIABILITY CENTERED MAINTENANCE PADA KOMPONEN KRITIS TURBIN UAP DI PT MADUBARU." JURNAL DISPROTEK 14, no. 2 (2023): 139–48. http://dx.doi.org/10.34001/jdpt.v14i2.4736.
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IMPLEMENTATION OF RELIABILITY CENTERED MAINTENANCE METHOD ON CRITICAL COMPONENTS OF STEAM TURBINE AT PT MADUBARUSteam turbine is a machine that can be used to convert heat energy from steam into mechanical energy so that it can produce electrical energy. As a source of electrical energy, PT Madubaru utilizes this steam turbine. The purpose of this research is to determine the Mean Time To Failure or MTTF and Mean Time To Repair or MTTR values and which components of this steam turbine have the highest Risk Priority Number (RPN) based on FMEA analysis. This study uses FMEA analysis and the Reliability Centered Maintenance (RCM) method. From the results of data processing that has been done it is known that the most critical component is the coupling component with a damage percentage level of 21.62% of the other components. Based on the calculation of the MTTF value, the result is 1863.74, this means that the coupling component needs to be maintained or checked after operating for 1863.74 hours or around 77,656 days. In addition, for the calculation of the MTTR value, the result is 0.7941 hours, which means that the coupling component takes 0.7941 hours to repair. While the FMEA analysis showed that the coupling component received the highest RPN value of 24 out of the other components. The cause that occurred was due to a broken coupling membrane which hampered the production process because it was necessary to repair the component.
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Ardiansyah Al Izzah, Mustari A Lamada, Zulhajji Zulhajji, Al Imran, and Riana T Mangesa. "ANALYSIS OF CONTROL SYSTEM RELIABILITY AT PLTG UPDK TELLO." Journal of Electrical Engineering and Informatics 2, no. 2 (2025): 70–79. https://doi.org/10.59562/jeeni.v2i2.6802.
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This study analyzes the reliability of the control system at the UPDK Tello Gas Power Plant (PLTG) with a focus on critical components that affect systemperformance. The evaluation was conducted using three main indicators: availability, Mean Time to Failure (MTTF), and Mean Time to Repair (MTTR), andtheir impact on operational efficiency. Quantitative descriptive research method was applied through Failure Mode and Effects Analysis (FMEA) analysis, direct observation, and maintenance document study. The results showed variations in reliability between components. In GE#1, the TCDA card has the highest RPN value of 156, availability 0.9995435874, MTTF 13139.99999 hours, MTTR 5.999999999 hours, reliability 0.1353352832, and operationalefficiency 99.95431704%. At GE#2, the exhaust thermocouple has the highest RPN of 192 with a reliability of 0.04978706857, while the 1.5 A fuse has the lowest availability of 0.9994675591, MTTF of 26279.99999 hours, MTTR of 14 hours, and operational efficiency of 99.94672755%. The average operational efficiency at GE#1 and GE#2 are 99.9866758% and 99.98731608% respectively. The results show that the system still meets operating standards although there are significant differences between the RPN values and other reliability indicators. Components with high RPN do not always have the lowest availability or operational efficiency. This research emphasizes the importance of a multi-indicator approach in reliability analysis as well as the need for customized maintenance strategies for each critical component.
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Panchal, Dilbagh, and Dixit Garg. "Application of Reliability-Centered Maintenance with a Computerized Maintenance Management System for the Wheelset in Rolling Stock." Spectrum of Decision Making and Applications 3, no. 1 (2025): 70–84. https://doi.org/10.31181/sdmap31202636.
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Maintenance plays an important role in ensuring the reliability and safety of rolling stock in the railway sector. This study focuses on the application of an integrated Reliability-Centered Maintenance (RCM) and Computerized Maintenance Management System (CMMS) for wheelsets in a wheel profiling measurement system. The combined use of RCM and CMMS helps optimize key reliability indices, such as Mean Time Between Failures (MTBF), Mean Time to Repair (MTTR), and Availability. The results demonstrate that automation enhances performance, with MTBF increasing from 500 to 800 hours and MTTR decreasing from 8 to 4 hours. Additionally, CMMS improved inventory management efficiency from 60% to 90% and reduced emergency repairs, resulting in annual savings of ₹2,325,000. Despite the higher initial investment of ₹6,375,000, automation proved more cost-effective over a five-year period. Furthermore, system availability increased from 97.3% to 99.1%, minimizing downtime and improving operational efficiency. These findings have been shared with the maintenance manager of the system under study for further implementation and testing.
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Zhou, Dong, Chuan Lv, and Yong Xiang Li. "A System’s MTTR Allocation Method Based on the Time Factors." Advanced Materials Research 430-432 (January 2012): 1910–13. http://dx.doi.org/10.4028/www.scientific.net/amr.430-432.1910.
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The maintenance allocation methods are important in the design and maintenance of product. However, these methods have lots of deficiencies in practical application, which is inconsistent with the purpose of the maintenance allocation. To allocate the system MTTR (Mean Time to Repair) to the subsystems more precisely and reasonably, a better model is needed. The aim of this paper is to develop a maintenance allocation model which can improve the applicability and operability, and solve the residue problem which exists in maintenance allocation. A simple case study is used to demonstrate how the model can be applied in a real case.
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Nur Halisa, Zidni Ilma, Fourry Handoko, and Sumanto Sumanto. "PENJADWALAN ULANG TERHADAP MESIN POMPA DISTRIBUSI AIR MENGGUNAKAN METODE PREVENTIVE MAINTENANCE (STUDI KASUS PERUMDA AIR MINUM TUGU TIRTA KOTA MALANG)." Jurnal Valtech 7, no. 1 (2024): 60–67. https://doi.org/10.36040/valtech.v7i1.9255.
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Perumda Air Minum Tugu Tirta Kota Malang mendistribusikan air kepada masyarakat secara terus menerus selama 24 jam dengan bantuan mesin pompa distribusi air. Dari hasil wawancara didapatkan data kerusakan mesin pompa sebanyak 10 kali dalam kurun waktu 1 (satu) tahun di Wendit 1, hal ini dikarenakan perawatan mesin yang kurang optimal. Tujuan penelitian ini membuat penjadwalan perawatan yang baru pada mesin pompa distribusi air di Perumda Air Minum Tugu Tirta Kota Malang menggunakan metode Preventive Maintenance dengan pendekatan Mean Time Between Failure (MTBF) dan Mean Time To Repair (MTTR) untuk memecahkan permasalahan penjadwalan perawatan pada mesin. Dari penelitian yang dilakukan didapatkan nilai rata-rata MTBF 4730,4 jam, MTTR 204,93 jam dan Availability didapatkan 76% dan standar mesin bekerja secara optimal adalah 80%. Maka mesin pompa belum bekerja secara optimal dan efektif berdasarkan jadwal perawatan dari perusahaan. Berdasarkan hasil wawancara dan perhitungan serta analisis data, diperoleh 2 saran untuk penjadwalan perawatan yang baru pada mesin pompa distribusi air. Untuk hasil wawancara mesin atau komponen yang mengalami corrective maintenance, dilakukan schedule preventive maintenance tiap 13 hari sekali, sedangkan hasil perhitungan data dilakukan tiap 1,3 bulan sekali. Mesin atau komponen yang mengalami breakdown maintenance dilakukan schedule peventive maintenance tiap 7 hari sekali. Rekomendasi penjadwalan perawatan mesin yang baru dapat dilakukan dengan menerapkan SOP perawatan mesin di Perumda Air Minum Tugu Tirta Kota Malang. Kata kunci: Penjadwalan Perawatan Mesin Pompa Distribusi Air, Preventive Maintenance (MTBF dan MTTR), Standar Operasional Prosedur (SOP)
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Tumanggor, Agustina, Joko Purnomo, and Muhammad Rizki. "The Reliability Analysis of Rotary Klin A." International Journal of Engineering Technology and Natural Sciences 2, no. 2 (2021): 70–76. http://dx.doi.org/10.46923/ijets.v2i2.80.
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XYZ is a company engaged in the production of iron ore into semi-finished products (Iron Reduction). The machine company has a very important and vital role to support the production process. Rotary Kiln is a place where iron ore reduction occurs, the result of which is called an iron reduction kiln or commonly known as sponge iron. By looking at the constraints at PT. XYZ on its production machines often breakdowns, which is a fundamental reason for researchers to increase the reliability level of the rotary kiln b PT XYZ production machine. XYZ. The research was carried out on the Rotary Kiln B machine made in Germany which plays a high enough role in the company. The research objective was to analyze the reliability (reliability) of Rotary Kiln b at PT. XYZ. The method used is Reliability with the Mean Time to Repair (MTTR) method. From the calculation of reliability on Rotary Kiln B with an operating time of 289 hours, the results are 0.5371 and MTTR 537.7732. The higher the reliability value, the better the machine used in the operation process.
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Wu, Zhenya, and Jianping Hao. "A Maintenance Task Similarity-Based Prior Elicitation Method for Bayesian Maintainability Demonstration." Mathematical Problems in Engineering 2020 (August 11, 2020): 1–19. http://dx.doi.org/10.1155/2020/2730691.
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Prior distribution elicitation is a challenging problem for a Bayesian inference-based mean time to repair (MTTR) demonstration because if inaccurate prior information is introduced into the prior distribution, the results become unreliable. This paper proposes a novel maintenance task representation model based on the similarity of attributed maintenance items. A novel similarity computation algorithm for maintenance tasks is then formulated on the basis of this model. Optimistic and pessimistic values are ascertained from the time data for similar maintenance tasks to obtain a prior distribution. The main idea is to separate maintenance tasks into distinct items and use attribute sets to extract key features. Each pair of items is then compared to quantify the differences between reference and candidate tasks. Candidate tasks with an acceptable difference from the reference task are taken as prior information sources for constructing the prior distribution. A case study involving a high-frequency (HF) transceiver MTTR Bayesian demonstration shows that the proposed method can effectively obtain maintenance tasks similar to those of information sources for prior distribution elicitation.
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Tota Pirdo Kasih. "Enhancing Operational Efficiency: A Study on Total Productive Maintenance for the Heidelberg Speed master 102V." Journal of Information Systems Engineering and Management 10, no. 3s (2025): 130–44. https://doi.org/10.52783/jisem.v10i3s.366.
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The aim of this intervention is centred on how Total Productive Maintenance (TPM – as commonly known in the literature) is applied on craftsmanship of the Heidelberg Speed master 102V offset printing machine with a view to improving the maintenance and consequently reducing the breakdown periods of the machine. The main aim is to assess how effective this management philosophy is in the reduction of degradation of equipment in the sector of printing media industry. The research methodology was quantitative, thus obtaining data from the maintenance records for the entire one-year period. Also, data for MTBF, MTTR, and machine availability (AVAIL) worked as key performance indicators. The result show that the use of TPM pursued almost all dimensions attributed with its usefulness in regard to not only the turnaround time of repair of machines but also the frequency of repairs. The results specifically support the assertion that there is a significant increase in production post the increase in the machine’s available time with the results of Regression Analysis MTBF=0.76, MTTR=0.68, OEE=0.72 and ANOVA producing F values MTBF=12.34, MTTR=15.67, OEE=9.45 which has p-value=<0.01 . Thus, it was concluded that TPM increases the maintenance initiatives in an organization and at the same time enhances the attainment of the organization’s industrial strategies. This is the rationale that supports how such a framework could be useful for organizations seeking to reduce the rate of maintenance and also enhance the levels of reliability of the equipment in the printing sector.
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O.C.Onuigbo and Edokpia R.O. "Tripping Profile of 11kv Feeders in Benin City, Edo State, Nigeria." Tripping Profile of 11kv Feeders in Benin City, Edo State, Nigeria 8, no. 12 (2023): 5. https://doi.org/10.5281/zenodo.10409799.
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Nigeria’s power supply has been experiencing incessant interruptions due to failures in the distribution system. The maintainability of the power system is important in meeting customers demand. The maintainability of three 11kv feeder in Benin Electricity Distribution Company (BEDC), Edo State Nigeria is evaluated in this study. The failure data which includes; time of failure, time outage was restored, causes of failure, and the failure time were collated and collected from the Injection substations of the three feeders for the year 2020 and 2021. Monthly and Yearly Mean time between failures (MTBF) and failure rate were calculated for the analysis. The analysis results revealed that the year 2021 had an increased failure rates than the year 2020 for the three feeders which implies a better performance. Keywords:- Failure Time, Repair Time, Maintainability, Mean Time to Repair (MTTR), and Repair Rate.
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Batubara, Sumiharni, and Enrico Azcauda Nainggolan. "Integrasi Penjadwalan Produksi dan Preventive Maintenance untuk Meminimasi Makespan dengan Menggunakan Metode Heijunka dan Batch – Backward Scheduling (Studi Kasus PT. BMC)." JURNAL TEKNIK INDUSTRI 8, no. 3 (2018): 159–71. http://dx.doi.org/10.25105/jti.v8i3.4731.
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PT BMC industry which produces automotive components. The problem, in the Brake Assembly Assembly Line, is that high engine downtime is caused by engine failure, resulting in production scheduling not going according to plan. The Drum Assembly Brake Line consists of BD III lines, producing Brake Drum and HUB III lines, producing Hubs. This study aims to determine the smallest and the same makespan value, for the BD III and HUB III lines using the integration of the Heijunka method and Batch Backward Scheduling with Preventive Maintenance (PM) Data processing begins by determining the sequence of jobs and completion time with Heijunka and Batch-Backward Scheduling method. Next, determine the MTTF and MTTR parameters to determine the optimal inspection interval and average repair time. In the last step, the scheduling results are integrated with the inspection and repair intervals if needed, to get the makespan value. The calculation results show that the optimal inspection interval and MTTR for the BD III line is 94 hours and 1.7 hours while the HUB III line is 116 hours with 1.2 hours. After integration of the Heijunka-PM method, the makespan is 511.8 hours and 510.3 hours for the BD III and HUB III lines. The integration of the Batch-Backward scheduling-PM method produces makespan of 538.8 hours and 540 hours for the BD III and HUB III lines. Based on the smallest makespan criteria, scheduling integration is chosen, using the Heijunka-PM method.
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Okirie, Ahiamadu Jonathan. "Maintenance Efficiency Optimization through Effective Leadership and Emotional Intelligence." International Journal of Research and Innovation in Applied Science IX, no. VI (2024): 147–56. http://dx.doi.org/10.51584/ijrias.2024.906013.
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This study examines the relationship between effective leadership, emotional intelligence (EI), and maintenance efficiency. It investigates the effects of leadership and EI initiatives on maintenance efficiency in three production facilities situated in Nigeria’s Niger Delta region. Important metrics such as Mean Time Between Failure (MTBF), Mean Time to Repair (MTTR), Downtime hours (DT), and Failure rate (λ) were employed in this study. Results reveal significant improvements in the employed metrics across all three facilities following the implementation of effective leadership and EI strategies. The case study facilities, EOC, OPP, and KGP experienced average improvement values of -73.25, -89.75, and -75.00 respectively. Despite variations in the level of improvement, the facilities witnessed enhancements in MTTR, MTBF, DT, and λ. The negative average improvement values signify improvements in maintenance efficiency post-implementation. The findings emphasize the positive impact of leadership and EI initiatives on maintenance practices and continuous improvement in organizations. By investing in leadership development and EI training, organizations can improve operational efficiency, optimal reliability, and cost reduction. The integration of these skills has significantly enhanced maintenance efficiency in the facilities studied, highlighting their significance in maintenance-intensive industries.
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Nurcahyo, Rahmat, Fahmi Wahyu Tri Nugroho, and Ellia Kristiningrum. "RELIABILITY, AVAILABILITY, AND MAINTAINABILITY (RAM) ANALYSIS FOR PERFORMANCE EVALUATION OF POWER GENERATION MACHINES." Jurnal Standardisasi 25, no. 1 (2023): 41. http://dx.doi.org/10.31153/js.v25i1.982.
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<p>This paper presents a reliability, availability, and maintenance (RAM) analysis framework to evaluate the performance of Power Generation Machines (MPL) as part of office infrastructure. MPL performance is evaluated by calculating maintenance performance, including Mean Time Between Failures (MTBF) to see reliability aspects, Mean Time to Repair (MTTR) to see maintainability aspects, and availability to see its operational availability. This research used data as maintenance log sheet data from three MPLs from January to September 2021. The results showed that MPL1 had the most optimal condition with an availability value of 93.48%, MPL2 was 82.19%, and MPL3 was 70.55%. Root cause analysis was carried out using Fishbone Diagram. It was able to identify the main problems in the machine's age and the procedure for submitting maintenance. Maintenance performance improvement is very much needed, especially the availability aspect for MPL2 and MPL3, as well as the valuation of existing preventive care. In addition, determining qualification standards for availability, MTBF, and machine MTTR needs to be carried out as a quantitative consideration in making MPL replacement decisions.<strong></strong></p><p> </p>
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Bajgotra, Sanjay, Sangam Sangral, Gurtej Singh, and Sanjeev Gupta. "MAINTENANCE COST ANALYSIS OF MECHANICAL COMPONENTS IN THE AGROCHEMICAL INDUSTRY." International Journal of Engineering Applied Sciences and Technology 9, no. 08 (2024): 61–66. https://doi.org/10.33564/ijeast.2024.v09i08.012.
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This study evaluates the total maintenance cost of mechanical components used in the agrochemical industry, focusing on identifying cost drivers, optimizing strategies, and analyzing performance factors. Key findings include differences in maintenance costs for 1000g and 500g Stock Keeping Units (SKUs) over three years from January 2021 to December 2023, highlighting significant trends in Mean Time Between Failures (MTBF), Mean Time to Repair (MTTR), and maintainability. The analysis integrates preventive, predictive, and corrective maintenance strategies, offering actionable insights to minimize costs and improve equipment reliability. This paper provides a framework for efficient resource allocation, reduced downtime, and enhanced operational efficiency.
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Suresh Kumar Sahani. "Performance and Assessment of Jaw Crusher in a Cement Manufacturing Plant." Communications on Applied Nonlinear Analysis 32, no. 3 (2025): 904–9. https://doi.org/10.52783/cana.v32.4855.
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Jaw crushers are essential apparatus in cement production facilities, principally utilized for pulverizing raw materials such as limestone, clay, and other minerals. The dependability of jaw crushers strongly influences production efficiency and maintenance expenses. This research examines the repair and failure rates of jaw crushers in a cement factory, assessing prevalent failure types, their underlying causes, and maintenance solutions to enhance equipment uptime. Data was gathered from maintenance logs over a two-year duration, and statistical analysis was conducted to ascertain Mean Time between Failures (MTBF) and Mean Time to Repair (MTTR). The results indicate that wear-related failures, such as jaw plate wear and bearing failures, constitute more than 60% of breakdowns, whereas lubrication problems contribute to 25% of failures. The use of predictive maintenance and the utilization of premium replacement parts can substantially save downtime and operating expenses.
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Nofri, Bambang, Anita Susilawati, and Romy Romy. "Optimization of Gas Turbine Maintenance Scheduling in PLN Tanjung Datuk Pekanbaru." Journal of Ocean, Mechanical and Aerospace -science and engineering- (JOMAse) 64, no. 3 (2020): 88–93. http://dx.doi.org/10.36842/jomase.v64i3.217.
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This study discusses determining the optimal scheduling for maintenance of gas turbine engines in PLN Tanjung Datuk Pekanbaru. The optimal maintenance scheduling is done on critical components, namely turbine blade and AVR (Automatic Voltage Regulator) using Monte Carlo simulation. The optimal scheduling maintenance scenario is done by generating random numbers from MTTF (Mean Time To Failure) and MTTR (Mean Time To Repair) values and data validity testing. The results of research for optimal checking of turbine engines are once every 10 days with the reliability of turbine engines 43%. The optimal time for repairing a gas turbine in case of damage is 1.49 hours. The checking time for critical components of the turbine blade is 9 days and AVR of 12 days. The scenario of preventive maintenance is likely need special repair or replacement periodically that is 117 days for turbine blade components and 173 days for AVR.
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Fatma, Nur Fadilah, Henri Ponda, and Trio Adi Saputra. "Perbaikan Perencanaan Penjadwalan Maintenance Pada Air Conditioner (AC) Menggunakan Metode Realibility Centered Maintenance (RCM) Di PT. Tifico Fiber Indonesia Tbk." Journal Industrial Manufacturing 7, no. 2 (2022): 103. http://dx.doi.org/10.31000/jim.v7i2.6935.
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In this era of global competition, companies are required to increase productivity in their companies so that they can compete with other companies. One of the indicators in increasing productivity is the level of reliability of the production machines in the company. In measuring how good the reliability of a production machine is, an effective and efficient maintenance process is needed for the company. In this case the Air Conditioner or hereinafter abbreviated as AC is one of the indoor air conditioning devices. One company that uses an Air Conditioner or AC to support the production process and the activities of its employees, especially those working in the production room, is PT Tifico Fiber Indonesia Tbk. The purpose of this study is to determine the critical components and the appropriate interval of Air Conditioner maintenance. Then a study was conducted to propose maintenance scheduling time intervals using the Reliability Centered Maintenance (RCM) method and to determine critical components using the Failure Mode Effect and Analysis (FMEA). From the research, it was found that the critical components of the splite type Air Conditioner component were the outdoor fan motor with an RPN value of 120, a compressor with an RPN value of 200, an outdoor capacitor with an RPN value of 140 and an indoor pcb with an RPN value of 168. Mean Time To Repair (MTTR), Mean Time To Failure (MTTF) Reliability Centered Maintenance (RCM) method obtained by outdoor fan motors for 13 days, compressors for 10 days, outdoor capacitors for 90 days and indoor pcb for 24 days.Keywords : Maintenance, Reliability Centered Maintenance (RCM), Mean Time To Repair (MTTR), Mean Time To Failure (MTTF), Failure Mode Effect and Analysis (FMEA).
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Shahin, Arash, Ashraf Labib, Soroosh Emami, and Mahdi Karbasian. "Improving Decision-Making Grid based on interdependence among failures with a case study in the steel industry." TQM Journal 31, no. 2 (2019): 167–82. http://dx.doi.org/10.1108/tqm-03-2018-0043.
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Purpose Decision-Making Grid (DMG) is used for determining maintenance tactics and is associated with the reliability and risk management of assets. In this grid, decision making is performed based on two indicators of Mean Time to Repair (MTTR) and frequency of failures. The purpose of this paper is to improve DMG by recognizing interdependence among failures. Design/methodology/approach Fault Tree Analysis and Reliability Block Diagram have been applied for improving DMG. The proposed approach has been examined on eight equipment of the steel making and continuous casting plant of Mobarakeh Steel Company. Findings Findings indicate different positions of equipment in the cells of the new grid compared to the basic grid. Research limitations/implications DMG is limited to two criteria of frequency of failures and MTTR values. In both basic and new DMGs, cost analysis has not been performed. The application of the proposed approach will help the reliability/maintenance engineers/analysts/managers to allocate more suitable maintenance tactics to equipment. This, in turn, will enhance the equipment life cycle and availability as the main objectives of physical asset management. Originality/value A major limitation of basic DMG is that the determined tactic based on these two indicators might not be an appropriate solution in all conditions, particularly when failures are interdependent. This has been resolved in this paper.
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Shahin, Arash, Nahid Aminsabouri, and Kamran Kianfar. "Developing a Decision Making Grid for determining proactive maintenance tactics." Journal of Manufacturing Technology Management 29, no. 8 (2018): 1296–315. http://dx.doi.org/10.1108/jmtm-12-2017-0273.
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Purpose The purpose of this paper is to further develop the Decision Making Grid (DMG) proposed by Ashraf Labib (e.g. Labib, 1998, 2004; Fernandez et al., 2003; Aslam-Zainudeen and Labib, 2011; Stephen and Labib, 2018; Seecharan et al., 2018) by proposing an innovative solution for determining proactive maintenance tactics based on mean time between failures (MTBF) and mean time to repair (MTTR) indicators. Design/methodology/approach First, the influence of MTTR and MTBF indicators on proactive maintenance tactics was computed. The tactics included risk-based maintenance (RBM), reliability-centered maintenance (RCM), total productive maintenance (TPM), design out maintenance (DOM), accessibility-centered maintenance (ACM) and business-centered maintenance (BCM). Then, the tactics were allocated to the cells of a DMG with MTTR and MTBF axes. The proposed approach was examined on 32 pieces of equipment of the Esfahan Steel Company and appropriate maintenance tactics were consequently determined. Findings The findings indicate that the DOM, BCM, RBM and ACM tactics with weights of 0.86, 0.94, 0.68 and 1.00 are located at the corners of the DMG, respectively. The two remaining tactics of TPM and RCM are located at the middle corners. Also, the results indicate that the share of tactics per spotted equipment in the grid as 62, 22 and 16 percent for RCM, DOM and BCM, respectively. Research limitations/implications While reactive and preventive maintenance strategies include corrective, prospective, predetermined, proactive and predictive policies, the focus of this study was merely on the tactics of proactive maintenance policy. The advantage of the developed DMG over Labib’s DMG lies in its application for equipment with the unique condition of the bathtub curve. Originality/value While the basic DMG has been mostly used regardless of the type of maintenance policies, this study provides a DMG for a specific application regarding the proactive policy. In addition, the heuristic approach proposed for the development of DMG distinguishes this study from other studies.
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Zhou, Dong, Xu Jia, and Yong Xiang Li. "RMS-Oriented Method of LRU Design." Advanced Materials Research 538-541 (June 2012): 3119–24. http://dx.doi.org/10.4028/www.scientific.net/amr.538-541.3119.
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Line Replaceable Unit (LRU) is an important unit of the equipments and products. Improving LRU planning can improve the characteristics of RMS of the products, especially on the maintainability and reducing life cycle service cost (LCSC). This paper studies the relationship between LRU planning and Product design, and builds the frame of LRU planning combined with RMS design rules and requirements. Qualitative analysis and quantitative analysis are adopted to analyze and evaluate the LRU planning synthetically. Analysis and evaluation process include three steps: qualitative analysis, Mean Time To Repair (MTTR) quantitative analysis and LCSC quantitative analysis. The mothed in this paper can improve the status quo of traditional product design out of touch with the RMS design.
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Darsini, Darsini, and Bayu Prabowo. "PERAWATAN MESIN SUCKER MULLER DI PT. DLH." Injection: Indonesian Journal of Vocational Mechanical Engineering 1, no. 1 (2021): 22–28. http://dx.doi.org/10.58466/injection.v1i1.75.
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DLH Sukoharjo merupakan industri tekstil yang bergerak dalam bidang pertenunan. Observasi dilakukan pada mesin Sucker Muller yang sering mengalami kemacetan pada bagian cilynder drier, kipas angin, beam stand dan bearing dan pada saat beam berputar tidak dapat memaksimalkan hasil produksi. khususnya pada bagian Weaving I dibagian Sizing. Permasalahan yangsering terjadi adalah cara perawatan mesin penganjian yang dilakukan oleh pabrik kurang efisien sehingga menimbulkan keterlambatan jumlah produksi. Dalam penyelesaian masalah dilakukan dengan cara perawatan mesin sucker muller tersebut. Metode preventive maintenance yang digunakan untuk perawatan dan perbaikan serta pencegahan kerusakan. Berdasarkan analisis diperoleh bahwa hasil Mean Time To Failure (M'I'I'F) komponen mesin Sucker Muller sebesar 6.63 jam dan nilai Mean Time To Repair (MTTR) 573.531 sebesar jam.
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Alfionita, Silvana, and Fakhri Ikhwanul Alifin. "Preventive Maintenance Analysis Based on Mean Time Between Failure (MTBF) and Mean Time to Repair (MTTR)." Angkasa: Jurnal Ilmiah Bidang Teknologi 15, no. 2 (2023): 201. http://dx.doi.org/10.28989/angkasa.v15i2.1833.
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A company's ability to meet its goals depends heavily on the smooth operation of its production process. To ensure that the production machinery operates smoothly and without any breakdowns, it is essential to implement effective maintenance that addresses any issues related to the equipment. This research aims to identify the root causes of equipment failure in the soda and dolomite bucket elevator by analyzing the Mean Time Between Failure (MTBF), Mean Time to Repair (MTTR), and conducting a qualitative evaluation using Failure Mode Effects Analysis (FMEA). The analysis reveals that the company performs preventive maintenance every 3 days, which is more frequent than the optimal schedule of once every 33 days. This suggests that the company may be addressing unforeseen failures. The FMEA analysis indicates that the highest Risk Priority Number is associated with suboptimal maintenance of the Staff. To address this issue, the company can use Radio-Frequency Identification (RFID) technology.
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Mo, Ruokun, and Weifeng Shi. "Ranking method of equipment failure risk in shipboard power system." Journal of Physics: Conference Series 2351, no. 1 (2022): 012035. http://dx.doi.org/10.1088/1742-6596/2351/1/012035.
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The realization of equipment failure risk ranking in shipboard power systems is of positive significance to the rapid recovery of ship power system faults. To effectively improve the speed and accuracy of ship fault recovery, accident plans are developed according to ranking results for the top n equipment with high risk. A risk ranking method for distribution equipment is proposed, which takes the outage power, failure probability, MTTR (mean time to repair), and equipment priority of distribution equipment as evaluation indexes, and uses the ENTROPY-TOPSIS analysis method. Taking the DDG-1000 destroyer as an example, the ranking of the failure risk of shipboard power distribution equipment is realized. The calculated results are consistent with the theoretical results, which proves the correctness of the proposed method.
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Mukul Garg. "The Unified Support-Engineering Flywheel: Data, AI and Shared Metrics to Maximize CSAT." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 2509–11. https://doi.org/10.32628/cseit25113368.
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This article introduces the Unified Support-Engineering Flywheel, a framework integrating data engineering, artificial intelligence (AI), and shared key performance indicators (KPIs) to dismantle organizational silos between support and engineering teams. By unifying support metrics (First Contact Resolution [FCR], Resolution Time), engineering metrics (Mean Time to Repair [MTTR], Log Error Rates), and Customer Satisfaction (CSAT) within a data lakehouse/mesh architecture, organizations can leverage AI-driven root cause analysis to proactively address systemic customer experience (CX) issues. Case studies demonstrate 25–40% improvements in CSAT within 6 months of implementation. The flywheel’s self-reinforcing cycle—where shared data enables faster issue resolution, improving CX and generating higher-quality data—proves critical for aligning cross-functional goals and maximizing customer satisfaction.
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Velmurugan, K., P. Venkumar, and R. Sudhakarapandian. "Performance Analysis of Tyre Manufacturing System in the SMEs Using RAMD Approach." Mathematical Problems in Engineering 2021 (May 27, 2021): 1–14. http://dx.doi.org/10.1155/2021/6616037.
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In the recent trends, production plants in the automobile industries all over the world are facing a lot of challenges to achieve better productivity and customer satisfaction due to increasing the passenger’s necessity and demand for transportation. In this direction, the belt, tyre, and tube manufacturing plants act as vital roles in the day-to-day life of the automobile industries. Tyre production plant comprises five major units, namely, raw material selection, preparation, tyre components, finishing, and inspection. The main purpose of this research is to implement the new method to predict the most critical subsystems in the tyre manufacturing system of the rubber industry. As mathematically, any one maintenance parameter among reliability, availability, maintainability, and dependability (RAMD) parameters is evaluated to identify the critical subsystems and their effect on the effectiveness of the tyre production system. In this research, the effect of variation in maintenance indices, RAMD, is measured to identify the critical subsystem of the tyre production system based on the mathematical modeling Markov birth-death approach (MBDA), and the equations of the subsystems are derived by using the Chapman–Kolmogorov method. Besides, it also calculates the performance of certain maintenance parameters concerning time such as mean time between failures (MTBF), mean time to repair (MTTR), and dependability ratio for each subsystem of the tyre production system. Finally, RAMD analysis of the tyre production systems has been executed for predicting the most critical subsystem by changing the rates of failure and repair of individual subsystems with the utilization of MATLAB software. RAMD analysis reveals that the subsystem bias cutting is most critical with the minimum availability of 0.8387, dependability 5.19, dependability ratio 0.8701, and maximum MTTR 38.46 hours of the subsystem. In this implementation of the proposed method, a real-time case study of the industrial repairable system of tyre manufacturing system has been taken for evaluating RAMD indices of the production plant of rubber industry cited in the southern region of Tamil Nadu, India.
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Jin, Liying, Wensheng Wang, HouYong Shu, Xuemei Ma, Chenxing Liang, and Qiang Ma. "Application Research on the Maintainability Allocation Method of a Certain Shooter Seat." Mathematical Problems in Engineering 2021 (December 29, 2021): 1–10. http://dx.doi.org/10.1155/2021/9376450.
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In view of the traditional maintainability allocation method for a certain shooter seat for maintainability allocation did not consider the lifecycle expense problem, the improved NSGA-II algorithm (iNSGA-II, for short) is adopted to establish a multiobjective comprehensive trade-off model for a certain shooter seat product lifecycle maintenance-related expenses and mean time to repair (MTTR, for short) and construct multiobjective optimization problem. The experimental results show that the Pareto optimal solution effectively solves the limitation of the traditional maintainability allocation method and then provides a basis for a certain shooter seat to obtain a reasonable maintainability allocation scheme. The superiority of the iNSGA-II algorithm to optimize the maintainability allocation of a certain shooter seat was verified by comparing it with the traditional maintainability allocation method.