Relevant bibliographies by topics / Predictive reliability

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Predictive reliability'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Predictive reliability"

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Mohamed Abdul Kadar Mohamed Jabarullah. "PredictNet: AI-enabled predictive maintenance system for telecommunications infrastructure reliability." World Journal of Advanced Research and Reviews 15, no. 3 (2022): 631–39. https://doi.org/10.30574/wjarr.2022.15.3.0954.

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This paper introduces PredictNet, a novel AI-enabled predictive maintenance system designed specifically for telecommunications infrastructure. The research addresses the critical challenge of maintaining reliability in increasingly complex telecom networks while reducing operational costs. Using machine learning algorithms and real-time sensor data, PredictNet demonstrates superior performance in predicting equipment failures before they occur. The system was implemented and tested on a mid-sized telecommunications network over a 12-month period, achieving 92.7% prediction accuracy with a mea
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Нікул, С., А. Дерев'янчук, О. Кравчук, Д. Максимчук та Ю. Сініло. "ВПРОВАДЖЕННЯ КОНЦЕПЦІЇ «PREDICTIVE MAINTENANCE» ДЛЯ ЗБІЛЬШЕННЯ НАДІЙНОСТІ ТА ПЕРЕДБАЧУВАНОСТІ РЕМОНТНИХ РОБІТ". Collection of scientific works of Odesa Military Academy, № 20 (14 грудня 2023): 51–55. http://dx.doi.org/10.37129/2313-7509.2023.20.51-55.

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This research article discusses the importance of implementing the Predictive Maintenance concept in the context of rocket and artillery weapons to improve the reliability and predictability of repair work. The article discusses key aspects of this concept, including its basic principles and methods, including the use of data analytics and artificial intelligence. The authors analyze the benefits of Predictive Maintenance in the military context, pointing to reduced repair costs, increased availability of military equipment, and extended equipment life. In addition, the article examines the ch
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Yang, Dezhen, Yidan Cui, Quan Xia, et al. "A Digital Twin-Driven Life Prediction Method of Lithium-Ion Batteries Based on Adaptive Model Evolution." Materials 15, no. 9 (2022): 3331. http://dx.doi.org/10.3390/ma15093331.

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Accurate life prediction and reliability evaluation of lithium-ion batteries are of great significance for predictive maintenance. In the whole life cycle of a battery, the accurate description of the dynamic and stochastic characteristics of life has always been a key problem. In this paper, the concept of the digital twin is introduced, and a digital twin for reliability based on remaining useful cycle life prediction is proposed for lithium-ion batteries. The capacity degradation model, stochastic degradation model, life prediction, and reliability evaluation model are established to descri
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Thota, Praveen Kumar. "Predictive Observability for Scalable Cloud Reliability Engineering." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–7. https://doi.org/10.55041/ijsrem28136.

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Because cloud computing environments support nearly all digital services, they need to be very dependable to maintain smooth delivery. Old-fashioned monitors only detect issues after something fails, which often causes services to be interrupted and downtime to be experienced. This framework is presented to help cloud reliability engineering by making future problems predictable. The way we do this is by reviewing active data, using special models and working with advanced techniques to find out about system problems before end users notice them. It uses flexible designs built for large cloud
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McCarthy, C. A., and Haijun Liu. "Predictive analysis ranks reliability improvements." IEEE Computer Applications in Power 12, no. 4 (1999): 35–40. http://dx.doi.org/10.1109/67.795136.

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Coolen, F. P. A., P. Coolen-Schrijner, and K. J. Yan. "Nonparametric predictive inference in reliability." Reliability Engineering & System Safety 78, no. 2 (2002): 185–93. http://dx.doi.org/10.1016/s0951-8320(02)00162-x.

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Grover, Arman, Debajyoti Roy Burman, Priyansh Kapaida, and Neelamani Samal. "Stock Market Price Prediction." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 591–95. http://dx.doi.org/10.22214/ijraset.2023.57184.

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Abstract: Investing in the stock market can be a convoluted and refined method of conducting business. Stock prediction is an extremely difficult and complex endeavor since stock values can fluctuate abruptly owing to a variety of reasons, making the stock market incredibly unpredictable.This paper explores predictive models for the stock market, aiming to forecast stock prices using machine learning algorithms. By analyzing historical market data and employing various predictive techniques, thestudy aims to enhance accuracy in predicting future stock movements. this paper contributes understa
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Holland, Christopher T., Shannon Tse, Cyrus P. Bateni, Dillon Chen, and Cassandra A. Lee. "MRI-Based Prediction of Meniscal Tear Repairability Demonstrates Limited Accuracy and Reliability." Journal of Clinical Medicine 14, no. 12 (2025): 4160. https://doi.org/10.3390/jcm14124160.

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Background: While magnetic resonance imaging (MRI) is commonly used to identify meniscal tears, intraoperative assessment typically dictates repairability. This study evaluated whether a simplified MRI-based scoring system could reliably predict meniscal repair versus meniscectomy. Methods: Patients who underwent meniscectomy or meniscal repair between 2010 and 2018 were retrospectively identified. Preoperative MRIs were independently reviewed in a blinded fashion by two radiologists and one orthopedic sports surgeon. Reviewers scored images based on four arthroscopic criteria for tear repaira
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Yang, Zhao Jun, Yin Kai Wang, Fei Chen, et al. "Prediction of Reliability Model for CNC Machine Tool Based on Exponential Smoothing Model." Advanced Materials Research 548 (July 2012): 495–99. http://dx.doi.org/10.4028/www.scientific.net/amr.548.495.

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In order to manage equipment maintenance work and reduce enterprise cost, a new prediction method of reliability parameters is proposed based on failure time in this paper. The reliability model was built based on failure time, and the reliability parameters were obtained by the empirical modeling method. Then parameters from historical data were used as predictive model parameters, which applied the exponential smoothing methods to establish predictive models based on historical data. Finally, prediction model of reliability was built by the predicted parameters used the above method. With fa
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Chandorkar, A., and A. Kharbanda. "Divergent Ensemble Networks : Improving Predictive Reliability and Computational Efficiency." International Journal of Artificial Intelligence & Applications 16, no. 1 (2025): 21–31. https://doi.org/10.5121/ijaia.2025.16102.

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The effectiveness of ensemble learning in improving prediction accuracy and estimating uncertainty is wellestablished. However, conventional ensemble methods often grapple with high computational demands and redundant parameters due to independent network training. This study introduces the Divergent Ensemble Network (DEN), a novel framework designed to optimize computational efficiency while maintaining prediction diversity. DEN achieves superior predictive reliability with reduced parameter overhead by leveraging shared representation learning and independent branching. Our results demonstra
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Dissertations / Theses on the topic "Predictive reliability"

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Aboalkhair, Ahmad M. "Nonparametric predictive inference for system reliability." Thesis, Durham University, 2012. http://etheses.dur.ac.uk/3918/.

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This thesis provides a new method for statistical inference on system reliability on the basis of limited information resulting from component testing. This method is called Nonparametric Predictive Inference (NPI). We present NPI for system reliability, in particular NPI for k-out-of-m systems, and for systems that consist of multiple ki-out-of-mi subsystems in series configuration. The algorithm for optimal redundancy allocation, with additional components added to subsystems one at a time is presented. We also illustrate redundancy allocation for the same system in case the costs of additio
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Abdel-Ghaly, A. A. "Analysis of predictive quality of software reliability models." Thesis, City University London, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.370836.

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Ossai, Chinedu Ishiodu. "Predictive modelling and reliability analysis of aged, corroded pipelines." Thesis, Curtin University, 2016. http://hdl.handle.net/20.500.11937/2561.

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Pipeline corrosion is a major challenge facing many oil and gas industries today because of the enormous downtime associated with corrosion related failures. This research utilized field data for modelling the reliability of aged corroded pipelines via probabilistic-based modelling techniques that included Markov decision process. The models developed from this work have been used for estimating the remaining useful life of corroded pipelines and can potentially enhance the integrity of future operational pipelines.
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Bryant, P. C. II. "Predictive validity and test-retest reliability of a measure of resilience." Thesis, Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/28883.

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Dikis, Konstantinos. "Establishment of a novel predictive reliability assessment strategy for ship machinery." Thesis, University of Strathclyde, 2017. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=27957.

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There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components. This holistic inspection and maintenance notion should be
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Sedghi, Mahdieh. "Data-driven predictive maintenance planning and scheduling." Licentiate thesis, Luleå tekniska universitet, Industriell Ekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-80828.

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The railway track network is one of the major modes of transportation and among a country’s most valuable infrastructure assets. Maintenance and renewal of railway infrastructure have a vital role in safety performance, the quality of the ride, train punctuality, and the life cycle cost of assets. Due to the large proportion of maintenance costs, increasing the efficiency of maintenance through optimised planning can result in high amounts of cost-saving. Moreover, from a safety perspective, late maintenance intervention can result in defective track and rollingstock components, which in sever
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Alberts, Philippus Petrus Hermanus. "The predictive validity of a selection battery for university bridging students in a public sector organisation / Philippus Petrus Hermanus Alberts." Thesis, North-West University, 2007. http://hdl.handle.net/10394/203.

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South Africa has faced tremendous changes over the past decade, which has had a huge impact on the working environment. Organisations are compelled to address the societal disparities between various cultural groups. However, previously disadvantaged groups have had to face inequalities of the education system in the past, such as a lack of qualified teachers (especially in the natural sciences), and poor educational books and facilities. This has often resulted in poor grade 12 results. Social responsibility and social investment programmes are an attempt to rectify these inequalities. The ob
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Borglund, Erik. "A predictive model for attaining quality in recordkeeping." Licentiate thesis, Mid Sweden University, Department of Information Technology and Media, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42.

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<p>Records are a subset of information and recordkeeping requirements demand that a record is managed with maintained authenticity and reliability, i.e. with high quality. Records are evidence of transactions and are used and managed in daily work processes. Records may be preserved for anything from milliseconds to eternity. With computer based information systems the electronic record was born: a record that is born digital. With electronic records problems regarding maintenance of authenticity and reliability have been identified. Electronic records are no longer physical entities as tradit
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Moen, Kurt Andrew. "Predictive modeling of device and circuit reliability in highly scaled CMOS and SiGe BiCMOS technology." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44700.

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The advent of high-frequency silicon-based technologies has enabled the design of mixed-signal circuits that incorporate analog, RF, and digital circuit components to build cost-effective system-on-a-chip solutions. Emerging applications provide great incentive for continued scaling of transistor performance, requiring careful attention to mismatch, noise, and reliability concerns. If these mixed-signal technologies are to be employed within space-based electronic systems, they must also demonstrate reliability in radiation-rich environments. SiGe BiCMOS technology in particular is positioned
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Dababneh, Amer Basheer. "A virtual predictive environment for monitoring reliability, life time, and maintainability of printed circuit boards." Thesis, University of Iowa, 2013. https://ir.uiowa.edu/etd/2470.

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This thesis highlights the development of an immerse and high fidelity virtual environment for lifetime and reliability analysis of circuit cards for nuclear power electronics. The developed virtual environment allows prediction of total life time, overall reliability and maintainability for circuit cards (system level) and their components (component level) through a simulation methodology. Component repair or replace approaches are used within this simulation which gives the user the ability to choose between them based
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Books on the topic "Predictive reliability"

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Kumar, Vijay, and Hoang Pham, eds. Predictive Analytics in System Reliability. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-05347-4.

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Doonan, Frank. A reliability and validity study of selection procedures in general, and a predictive validity study of Saville and Holdsworth's occupational personality questionnaire - Concept 4. The Author], 1991.

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Pandey, Ajeet Kumar, and Neeraj Kumar Goyal. Early Software Reliability Prediction. Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-1176-1.

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Melchers, Robert E., and André T. Beck, eds. Structural Reliability Analysis and Prediction. John Wiley & Sons Ltd, 2017. http://dx.doi.org/10.1002/9781119266105.

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Klyatis, Lev M., and Edward L. Anderson, eds. Reliability Prediction and Testing Textbook. John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119411949.

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Musa, John D. Software reliability: Measurement, prediction, application. McGraw-Hill, 1987.

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Anthony, Iannino, and Okumoto Kazuhira, eds. Software reliability: Measurement, prediction, application. McGraw-Hill, 1990.

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United States. Army Communications Research and Development Command. Reliability prediction of electronic equipment. Department of Defense, 1986.

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United States. National Aeronautics and Space Administration., ed. Measurement-based reliability prediction methodology. Center for Reliable and High-Performance Computing, Coordinated Science Laboratory, College of Engineering, University of Illinois at Urbana-Champaign, 1991.

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United States. National Aeronautics and Space Administration., ed. Measurement-based reliability prediction methodology. Center for Reliable and High-Performance Computing, Coordinated Science Laboratory, College of Engineering, University of Illinois at Urbana-Champaign, 1991.

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Book chapters on the topic "Predictive reliability"

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Majumdar, Rana, Subhrapratim Nath, and Vijay Kumar. "Role of MCDM in Software Reliability Engineering." In Predictive Analytics. CRC Press, 2020. http://dx.doi.org/10.1201/9781003083177-1.

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Muthuraj, R. Jeromia, A. Mohamed Ashik, and A. Loganathan. "Statistical Methodology for Software Reliability with Environmental Factors." In Predictive Analytics. CRC Press, 2020. http://dx.doi.org/10.1201/9781003083177-14.

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Patil, Suyog S., Anand K. Bewoor, Rajkumar Bhimgonda Patil, and Mohamed Arezki Mellal. "Maintenance Data-Trends Based Reliability Availability and Maintainability (RAM) Assessment of a Steam Boiler." In Predictive Analytics. CRC Press, 2020. http://dx.doi.org/10.1201/9781003083177-15.

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McClarren, Ryan G. "Reliability Methods for Estimating the Probability of Failure." In Uncertainty Quantification and Predictive Computational Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99525-0_8.

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Johnson, Richard A., James Evans, and David Green. "Predictive Distributions of Order Statistics." In Recent Advances in Life-Testing and Reliability. CRC Press, 2023. http://dx.doi.org/10.1201/9781003418313-27.

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Timashev, Sviatoslav, and Anna Bushinskaya. "Methodology of Predictive Maintenance of Pipelines." In Diagnostics and Reliability of Pipeline Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25307-7_9.

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Metzger, Andreas, and Felix Föcker. "Predictive Business Process Monitoring Considering Reliability Estimates." In Advanced Information Systems Engineering. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59536-8_28.

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Pinder, J. E., K. W. McLeod, and D. C. Adriano. "Particulate Transport Processes in Agroecosystems: Validation of Predictive Models." In Reliability of Radioactive Transfer Models. Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-009-1369-1_16.

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Karimi, Naghmeh, Jean-Luc Danger, Florent Lozac’h, and Sylvain Guilley. "Predictive Aging of Reliability of Two Delay PUFs." In Security, Privacy, and Applied Cryptography Engineering. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49445-6_12.

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Dimov, Aleksandar. "Empirical Analysis of Software Reliability Models Predictive Ability." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23163-6_20.

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Conference papers on the topic "Predictive reliability"

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Sirri, Naoufal, and Christophe Guyeux. "Firefighter Intervention Predictive Modeling: Reliability Assessment." In 2024 International Conference on Circuit, Systems and Communication (ICCSC). IEEE, 2024. http://dx.doi.org/10.1109/iccsc62074.2024.10617206.

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Fischer, Paula, and Martin Dazer. "Evaluating Success Conditions for Predictive Maintenance." In 2025 Annual Reliability and Maintainability Symposium (RAMS). IEEE, 2025. https://doi.org/10.1109/rams48127.2025.10935273.

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Maillard, Pierre-François, Sudipta Das, and Bimal Kumar Roy. "Minimal Adversarial Attacks on Predictive Maintenance." In 2024 8th International Conference on System Reliability and Safety (ICSRS). IEEE, 2024. https://doi.org/10.1109/icsrs63046.2024.10927430.

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Karwa, Roshan R., Gajendra R. Bamnote, Yogita A. Dhumale, Pranita P. Deshmukh, Rupali A. Meshram, and Sumera M. Iqbal. "Predictive Maintenance: Machine Learning Approaches for Enhanced Equipment Reliability." In 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI). IEEE, 2024. https://doi.org/10.1109/idicaiei61867.2024.10842946.

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Dhakal, Rabin, Kushal Buch, Wayne Li, and Curtiss Fox. "Solar PV Inverter Retrofitting: Enhancing Reliability Through Predictive Maintenance." In 2024 IEEE 52nd Photovoltaic Specialist Conference (PVSC). IEEE, 2024. http://dx.doi.org/10.1109/pvsc57443.2024.10749626.

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Hossini, Anas, Leïla Kloul, Maël Guiraud, and Benjamin Cohen Boulakia. "Predictive Maintenance for Smart Buildings: Balancing QoS and Cost Efficiency." In 2025 Annual Reliability and Maintainability Symposium (RAMS). IEEE, 2025. https://doi.org/10.1109/rams48127.2025.10935070.

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Zhou, Haozhou, Wei Li, Srinivasan Radhakrishnan, and Sagar Kamarthi. "Feature Engineering Toolkit for Predictive Analytics in Engineering and Healthcare Informatics." In 2025 Annual Reliability and Maintainability Symposium (RAMS). IEEE, 2025. https://doi.org/10.1109/rams48127.2025.10935234.

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Kapoor, Dimple, Deepali Gupta, and Mudita Uppal. "IoT- driven Predictive Maintenance for Enhanced Reliability in Industrial Applications." In 2024 International Conference on Intelligent & Innovative Practices in Engineering & Management (IIPEM). IEEE, 2024. https://doi.org/10.1109/iipem62726.2024.10925824.

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Milasheuski, Usevalad, Piero Baraldi, Enrico Zio, and Stefano Savazzi. "Federated Generative Models for Predictive Maintenance in Industrial Environments." In 2024 8th International Conference on System Reliability and Safety (ICSRS). IEEE, 2024. https://doi.org/10.1109/icsrs63046.2024.10927496.

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Liu, Naijia, and Gianluca Tempesti. "Predictive Age-Aware Runtime Remapping for Many-Core Systems." In 2024 8th International Conference on System Reliability and Safety (ICSRS). IEEE, 2024. https://doi.org/10.1109/icsrs63046.2024.10927459.

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Reports on the topic "Predictive reliability"

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Marangoni-Simonsen, David. Development of Predictive Models for Fleet Reliability in the Aircraft Industry. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1761807.

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Peterson, Warren. PR-663-19600-Z01 Develop Guidance for Calculation of HCDP in Pipelines. Pipeline Research Council International, Inc. (PRCI), 2020. http://dx.doi.org/10.55274/r0011659.

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To maintain the integrity and reliability of natural gas transportation systems, system operators ensure that products in transit remain in the gas phase under foreseeable operating conditions. Compliance with pipeline hydrocarbon dew point (HCDP) specifications are demonstrated though in-situ testing or predictive models based on Equations of State (EOS) calculations. Numerical prediction of HCDP is a product of contributing elements, including gas chromatography, calibration gas quality, thermophysical science and the experimental data that underpins equations of state. Some hydrocarbon mixt
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O'Brien, James G., Emily L. Barrett, Xiaoyuan Fan, Ruisheng Diao, Renke Huang, and Qiuhua Huang. Adaptive RAS/SPS System Settings for Improving Grid Reliability and Asset Utilization through Predictive Simulation and Controls. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1580707.

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Han, Katherine, John Lippiatt, Yu-Chen Shen, et al. Predictive Models and Novel Accelerated Tests for the Reliability of Cell Metallization and Solder Joints in Photovoltaic Modules. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1804456.

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Kumar, Aditya, and George Boutselis. Transient Efficiency Flexibility and Reliability Optimization of Coal-Fired Power Plants - Report on Model-Predictive Control Library Development. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1856859.

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Willson. L51756 State of the Art Intelligent Control for Large Engines. Pipeline Research Council International, Inc. (PRCI), 1996. http://dx.doi.org/10.55274/r0010423.

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Computers have become a vital part of the control of pipeline compressors and compressor stations. For many tasks, computers have helped to improve accuracy, reliability, and safety, and have reduced operating costs. Computers excel at repetitive, precise tasks that humans perform poorly - calculation, measurement, statistical analysis, control, etc. Computers are used to perform these type of precise tasks at compressor stations: engine / turbine speed control, ignition control, horsepower estimation, or control of complicated sequences of events during startup and/or shutdown. For other task
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Kempe, Michael. Predictive Models and Novel Accelerated Tests for the Reliability of Cell Metallization in Photovoltaic Modules: Cooperative Research and Development Final Report, CRADA Number CRD-16-00603. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2328550.

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Gates, R. K., G. J. Gibson, and K. K. McLain. Reliability Growth Prediction. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada176128.

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Nasr, Elhami, Tariq Shehab, Nigel Blampied, and Vinit Kanani. Estimating Models for Engineering Costs on the State Highway Operation and Protection Program (SHOPP) Portfolio of Projects. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2365.

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The State Highway Operation and Protection Program (SHOPP) is crucial for maintaining California’s 15,000-mile state highway system, which includes projects like pavement rehabilitation, bridge repair, safety enhancements, and traffic management systems. Administered by Caltrans, SHOPP aims to preserve highway efficiency and safety, supporting economic growth and public safety. This research aimed to develop robust cost-estimating models to improve budgeting and financial planning, aiding Caltrans, the California Transportation Commission (CTC), and the Legislature. The research team collected
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Fan, Xiaoyuan, Renke Huang, Qiuhua Huang, et al. Adaptive RAS/SPS System Setting for Improving Grid Reliability and Asset Utilization through Predictive Simulation and Controls: A Use Case for Transformative Remedial Action Scheme Tool (TRAST): Jim Bridger RAS Evaluation and Analysis. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1604168.

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