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Journal articles on the topic 'Vibration-based condition monitoring'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 June 2025

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Oba, Takuya, Koichi Yamada, Hitoshi Soma, and Katsuya Tanifuji. "356776 CONDITION MONITORING FOR SHINKANSEN BOGIES BASED ON VIBRATION ANALYSIS(Condition Monitoring,Technical Session)." Proceedings of International Symposium on Seed-up and Service Technology for Railway and Maglev Systems : STECH 2009 (2009): _356776–1_—_356776–6_. http://dx.doi.org/10.1299/jsmestech.2009._356776-1_.

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Carden, E. Peter, and Paul Fanning. "Vibration Based Condition Monitoring: A Review." Structural Health Monitoring: An International Journal 3, no. 4 (2004): 355–77. http://dx.doi.org/10.1177/1475921704047500.

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Senapaty, Goutam, and U. Sathish Rao. "Vibration based condition monitoring of rotating machinery." MATEC Web of Conferences 144 (2018): 01021. http://dx.doi.org/10.1051/matecconf/201814401021.

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This project looks at the different maintenance philosophies and the importance of vibration analysis in predictive maintenance. Since most industries and plants make use of rotational equipment, vibration analysis plays a major role in detecting machine defects and developing flaws before the equipment fails and potentially damages other related equipment and to avoid unwanted breakdowns and downtime. Vibration analysis can help increase the lifetime of equipment when the faults are diagnosed at the right time. Vibration analysis of a rotating table top model is also done to show that some faults might exist even though they are not visible to the naked eye.

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Senapaty, Goutam, and U. Sathish Rao. "Vibration based condition monitoring of rotating machinery." MATEC Web of Conferences 144 (2018): 01021. http://dx.doi.org/10.1051/matecconf/201714401021.

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Ivanov, Sergiy, and Pavlo Oliinyk. "MEMS-BASED WIRELESS VIBRATION TRANSDUCER FOR CONDITION MONITORING." Information and Telecommunication Sciences, no. 1 (June 30, 2022): 56–65. https://doi.org/10.20535/2411-2976.12022.56-65.

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Background. When monitoring vibration of rotating machines, especially heavy ones, problems with cables of transducers often emerge. Those cables are usually long, heavy and prone to damage. Objective. The purpose of the paper is to develop a wireless vibration transducer, which is free of those problems, on the base of MEMS accelerometer. Sensor developed should provide low power consumption, linear frequency response at least in 10…1000 Hz range, calculate vibration RMS and detect machine condition based on it. Methods. Develop wireless sensor design based on 8-bit MCU. Develop method of MEMS frequency response correction, based on spectral analysis. Compare sensor developed with industrial piezoelectric ones. Results. Transducer developed can be used instead of the industrial piezoelectric vibration transducers. Moreover, MEMS-based transducer allows one to move basic machine condition detection process from the high–level system to transducer level. That, in turn, allows one to reduce network traffic and simplify condition monitoring system as a whole. Conclusions. MEMS-based wireless vibration transducer for condition monitoring is developed. Tests conducted showed that the transducer developed is well–behaved and its precision is comparable to one of industrial piezoelectric transducers.

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Kappatos, Vassilios, and Konstantinos Chatzitheodorou. "Terminology study on vibration-based condition monitoring technique." Vibroengineering PROCEDIA 34 (November 5, 2020): 20–26. http://dx.doi.org/10.21595/vp.2020.21758.

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Hasegawa, Takanori, Mao Saeki, Tetsuji Ogawa, and Teppei Nakano. "Vibration-Based Fault Detection for Flywheel Condition Monitoring." Procedia Structural Integrity 17 (2019): 487–94. http://dx.doi.org/10.1016/j.prostr.2019.08.064.

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Li, Jian. "Sensing of Driving Conditions Based on Vibration Signal." Applied Mechanics and Materials 477-478 (December 2013): 105–8. http://dx.doi.org/10.4028/www.scientific.net/amm.477-478.105.

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The mechanical state recognition process, the feature parameter vector problem of high dimensionality, this paper designed a system to achieve the mechanical condition of the driving conditions online monitoring and evaluation. The system has accumulated a large number of driving condition monitoring data for data mining provides a data source, but also to ensure all of this paper's findings authentic.

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Hassan, Ietezaz Ul, Krishna Panduru, and Joseph Walsh. "Review of Data Processing Methods Used in Predictive Maintenance for Next Generation Heavy Machinery." Data 9, no. 5 (2024): 69. http://dx.doi.org/10.3390/data9050069.

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Vibration-based condition monitoring plays an important role in maintaining reliable and effective heavy machinery in various sectors. Heavy machinery involves major investments and is frequently subjected to extreme operating conditions. Therefore, prompt fault identification and preventive maintenance are important for reducing costly breakdowns and maintaining operational safety. In this review, we look at different methods of vibration data processing in the context of vibration-based condition monitoring for heavy machinery. We divided primary approaches related to vibration data processing into three categories–signal processing methods, preprocessing-based techniques and artificial intelligence-based methods. We highlight the importance of these methods in improving the reliability and effectiveness of heavy machinery condition monitoring systems, highlighting the importance of precise and automated fault detection systems. To improve machinery performance and operational efficiency, this review aims to provide information on current developments and future directions in vibration-based condition monitoring by addressing issues like imbalanced data and integrating cutting-edge techniques like anomaly detection algorithms.

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Chebolu, Rohini Kumar, and Pujari Satish. "CONDITION MONITORING AND DYNAMIC BALANCING OF A HOT AIR CIRCULATION BLOWER BY VIBRATION TOOL." International Journal of Engineering Sciences & Research Technology 5, no. 3 (2016): 40–49. https://doi.org/10.5281/zenodo.46986.

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Over the years machinery health management has become vital part of the plant operation. Earlier day’s machinery maintenance is only focused on reactive maintenance. In later stages, Vibration Monitoring is a critical component of any Predictive Maintenance (PdM) Practices. Vibration Monitoring and subsequent analysis has helped in identifying an earlier consequence of breakdowns. [1]The idea of performing Predictive Maintenance to perform maintenance on the machines they exhibit signs of mechanical failure has become known as Condition Based Maintenance (CBM). Condition Based Monitoring System (CBMS) is proven technology to be less costly than the failure. A simple Consequence of Failure Analysis (CFA) is made to justify preventive maintenance activities. This evolutionary process of machinery maintenance has allowed the maintenance operation to more “Proactive” than reactive in their maintenance tasking. This paper pertains on a Main Circulation Blower which extremely critical for their production. We have observed the machinery health condition based on vibration measurements and vibration analysis which really helped us in identifying a failure sequence. In-situ dynamic balancing of main circulation blower ,which focuses the importance vibration analysis to reduce the induced vibrations from unbalance forces and Significant reduction in the vibration levels and which increased the machinery availability for production.

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Kuzin, Evgeny, Vladimir Bakin, and Dmitriy Dubinkin. "Mining Equipment Technical Condition Monitoring." E3S Web of Conferences 41 (2018): 03020. http://dx.doi.org/10.1051/e3sconf/20184103020.

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The Earth, being the main object and operational basis for mining, is exposed to the greatest impact because of extracting minerals. Protection of elements of the biosphere, including subsoil, should provide for the provision of scientifically based and economically justified completeness and complexity of use. The article discusses the need to monitor the technical condition of mining equipment, as applied to assessing its technical condition and reducing energy consumption by this equipment. The dependence of energy consumption on vibration parameters and temperature of equipment surfaces is shown. The data of the results of vibration parameters monitoring are given. Criteria are given for estimating the energy efficiency of operation of process equipment and, accordingly, the influence of these parameters on the environment.

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Wang, Liuhuo, Chengfeng Liu, Xiaowei Zhu, Zhixian Xu, Wenwei Zhu, and Long Zhao. "Active Vibration-Based Condition Monitoring of a Transmission Line." Actuators 10, no. 12 (2021): 309. http://dx.doi.org/10.3390/act10120309.

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In the power system, the transmission tower is located in a variety of terrains. Sometimes there will be displacement, inclination, settlement and other phenomena, which eventually lead to the collapse of the tower. In this paper, a method for monitoring the settlement of a transmission tower based on active vibration response is proposed, which is based on the principle of modal identification. Firstly, a device was designed, which includes three parts: a monitoring host, wireless sensor and excitation device. It can tap the transmission tower independently and regularly, and collect the vibration response of the transmission tower. Then, vibration analysis experiments were used to validate the horizontal vibration responses of transmission towers which can be obtained by striking the transmission towers from either the X direction or Y direction. It can be seen from the frequency response function that the natural frequencies obtained from these two directions are identical. Finally, the transmission tower settlement experiment was carried out. The experimental results show that the third to fifth natural frequencies decreased most obviously, even up to 2.83 Hz. Further, it was found that under different conditions, as long as the tower legs adjacent to the excitation position settle, the natural frequency will decrease more significantly, which is very helpful for engineering application.

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Raiter, Mr Siddikiakbar S. "Split Air-Conditioned Condition Monitoring Using Mechatronics Sensors." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47048.

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Abstract - This study presents a mechatronics-based approach for real-time condition monitoring of split air-conditioning systems. Integrating sensors and IoT technologies, the proposed system enables remote monitoring and fault diagnosis of critical parameters such as temperature, humidity, pressure, and vibration. Mechatronics sensors, including temperature, pressure, gas sensor and vibration sensors, are strategically deployed to collect data on system performance. Advanced signal processing and machine learning algorithms analyze the data to detect anomalies, predict potential faults, and optimize system efficiency. The system provides real-time alerts and recommendations for maintenance, reducing downtime and energy consumption. Experimental results demonstrate the effectiveness of the proposed approach in Improving system reliability, efficiency, and overall performance. Also with the help of sensor and some arrangements in split Air-conditioned air exchange done to reduce indoor pollution like excess of co2 and other pollutants. With help of mechatronics arrangement self cleaning from dust and give previous information of this cleaning. Key Words: Condition Monitoring, Mechatronics Sensors, Split Air-Conditioning, IoT, Predictive Maintenance, Energy Efficiency, indoor pollution, self cleaning.

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Yunusa-kaltungo, Akilu, and Jyoti K. Sinha. "Effective vibration-based condition monitoring (eVCM) of rotating machines." Journal of Quality in Maintenance Engineering 23, no. 3 (2017): 279–96. http://dx.doi.org/10.1108/jqme-08-2016-0036.

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Purpose The purpose of this paper is mainly to highlight how a simplified and streamlined approach to the condition monitoring (CM) of industrial rotating machines through the application of frequency domain data combination can effectively enhance the eMaintenance framework. Design/methodology/approach The paper commences by providing an overview to the relevance of maintenance excellence within manufacturing industries, with particular emphasis on the roles that rotating machines CM of rotating machines plays. It then proceeds to provide details of the eMaintenance as well as its possible alignment with the introduced concept of effective vibration-based condition monitoring (eVCM) of rotating machines. The subsequent sections of the paper respectively deal with explanations of data combination approaches, experimental setups used to generate vibration data and the theory of eVCM. Findings This paper investigates how a simplified vibration-based rotating machinery faults classification method based on frequency domain data combination can increase the feasibility and practicality of eMaintenance. Research limitations/implications The eVCM approach is based on classifying data acquired under several experimentally simulated conditions on two different machines using combined higher order signal processing parameters so as to reduce CM data requirements. Although the current study was solely based on the application of vibration data acquired from rotating machines, the knowledge exchange platform that currently dominates present day scientific research makes it very likely that the lessons learned from the development of eVCM concept can be easily transferred to other scientific domains that involve continuous CM such as medicine. Practical implications The concept of eMaintenance as a cost-effective and smart means of increasing the autonomy of maintenance activities within industries is rapidly growing in maintenance-related literatures. As viable as the concept appears, the achievement of its optimum objectives and full deployment to the industry is still subjective due to the complexity and data intensiveness of conventional CM practices. In this paper, an eVCM approach is proposed so that rotating machine faults can be effectively detected and classified without the need for repetitive analysis of measured data. Social implications The main strength of eVCM lies in the fact that it permits the sharing of historical vibration data between identical rotating machines irrespective of their foundation structures and speed differences. Since eMaintenance is concerned with driving maintenance excellence, eVCM can potentially contribute towards its optimisation as it cost-effectively streamlines faults diagnosis. This therefore implies that the simplification of vibration-based CM of rotating machines positively impacts the society with regard to the possibility of reducing how much time is actually spent on the accurate detection and classification of faults. Originality/value Although the currently existing body of literature already contains studies that have attempted to show how the combination of measured vibration data from several industrial machines can be used to establish a universal vibration-based faults diagnosis benchmark for incorporation into eMaintenance framework, these studies are limited in the scope of faults, severity and rotational speeds considered. In the current study, the concept of multi-faults, multi-sensor, multi-speed and multi-rotating machine data combination approach using frequency domain data fusion and principal components analysis is presented so that faults diagnosis features for identical rotating machines with different foundations can be shared between industrial plants. Hence, the value of the current study particularly lies in the fact that it significantly highlights a new dimension through which the practical implementation and operation of eMaintenance can be realized using big data management and data combination approaches.

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OBA, Takuya, Koichi YAMADA, Nobuyuki OKADA, Hitoshi SOMA, and Katsuya TANIFUJI. "Condition Monitoring for Shinkansen Bogies Based on Vibration Analysis." Transactions of the Japan Society of Mechanical Engineers Series C 75, no. 757 (2009): 2459–67. http://dx.doi.org/10.1299/kikaic.75.2459.

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Zonta, Daniele, Matteo Pozzi, Marco Forti, and Paolo Zanon. "Vibration-Based Condition Monitoring of Smart Prefabricated Concrete Elements." Key Engineering Materials 293-294 (September 2005): 743–52. http://dx.doi.org/10.4028/www.scientific.net/kem.293-294.743.

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The University of Trento is promoting a research effort aimed at developing an innovative distributed construction system based on smart prefabricated concrete elements that can allow real-time assessment of the condition of bridge structures. So far, two reduced-scale prototypes have been produced, each consisting of a 0.2×0.3×5.6m RC beam specifically designed for permanent instrumentation with 8 long-gauge Fiber Optics Sensors (FOS) at the lower edge. The sensors employed are FBG-based and can measure finite displacements both in statics and dynamics. The acquisition module uses a single commercial interrogation unit and a softwarecontrolled optical switch, allowing acquisition of dynamic multi-channel signals from FBG-FOS, with a sample frequency of 625 Hz per channel. The performance of the system is undergoing validation in the laboratory. The scope of the experiment is to correlate changes in the dynamic response of the beams with different damage scenarios, using a direct modal strain approach. Each specimen is dynamically characterized in the undamaged state and in different condition states, simulating different cracking levels. The location and the extent of damage are evaluated through the calculation of damage indices which take into account changes in frequency and in strain-modeshapes. This paper presents in detail the results of the experiment as conducted on one of these prototypes and demonstrates how the damage distribution detected by the system is fully compatible with the damage extent appraised by inspection.

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OBA, Takuya, Koichi YAMADA, Nobuyuki OKADA, and Katsuya TANIFUJI. "Condition Monitoring for Shinkansen Bogies Based on Vibration Analysis." Journal of Mechanical Systems for Transportation and Logistics 2, no. 2 (2009): 133–44. http://dx.doi.org/10.1299/jmtl.2.133.

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Valentín, D., A. Presas, M. Egusquiza, E. Egusquiza, and JL Drommi. "Innovative Approaches to Hydraulic Turbine Advanced Condition Monitoring." IOP Conference Series: Earth and Environmental Science 1411, no. 1 (2024): 012019. https://doi.org/10.1088/1755-1315/1411/1/012019.

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Abstract This research paper introduces advancements in hydraulic turbine condition monitoring through the implementation of vibration-based condition indicators. Conventional methods rely on vibration monitoring systems, triggering alarms or unit trips based on overall measured values at specific points. However, these methods may generate false alarms under changing operating conditions, such as variations in head or flow rate. In response, our study focuses on the development and validation of condition indicators derived from vibration data, considering the turbine’s operating conditions. Our methodology defines and validates these condition indicators, providing a detailed understanding of the hydraulic turbine’s condition. By employing an enhanced condition monitoring framework, our approach not only improves fault detection accuracy but also offers a comprehensive perspective on the overall health of the turbine. Experimental results from a prototype Kaplan turbine showcase the effectiveness of our methodology in detecting and characterizing faults, enabling proactive maintenance strategies. The Kaplan turbine studied is one of the demonstrators of the XFLEX HYDRO project. In conclusion, this research delivers a valuable tool for industry professionals and researchers involved in hydraulic turbine maintenance and optimization.

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Mahapatra, Rashmita K., Shalini J. Yadav, and Rajan Yadav. "Laser Based Vibration Sensor Through Mobile." International Journal of Applied Sciences and Smart Technologies 5, no. 1 (2023): 67–74. http://dx.doi.org/10.24071/ijasst.v5i1.4695.

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Machine condition monitoring has gained momentum over the years and becoming an essential component in the today’s industrial units. A cost-effective machine condition monitoring system is need of the hour for predictive maintenance. The paper presents the design and implementation using vibration sensor, and also this system operated through smartphones. Vibration analysis plays a major role in detecting machine defects and developing flaws before the equipment fails and potentially damages. The concept of this project was to detect faulty equipment in industry machine so that before damaging the whole machine faulty equipment can be replace and improve the durability of machine.

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Yakhshiev, Sherali, Ilkhom Egamberdiev, Akmal Mamadiyarov, Maruf Saibov, and Nazokat Karimova. "Development of technology and methodology for monitoring the technical condition of metalcutting machines." E3S Web of Conferences 525 (2024): 05021. http://dx.doi.org/10.1051/e3sconf/202452505021.

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This work was carried out with the aim of developing the basic provisions of an automated vibration monitoring system for metal-cutting equipment. The paper presents the theoretical foundations of vibration monitoring technology, which uses regular measurements of vibration parameters, their frequency analysis, and mathematical modeling of degradation processes in machines. Vibration monitoring technology allows to determine the time points necessary for repair and maintenance of equipment based on its actual condition. Applications of vibration monitoring are reducing losses associated with equipment failures and reducing maintenance and repair costs, as well as increasing the safety of work. The most relevant is the implementation of a vibration monitoring system when diagnosing the technical condition of bearings and gears (metal-cutting machines, hydraulic systems). As a result of the research, diagnostic models of changes in the technical condition of metal-cutting machines and vibration characteristics of typical defects were obtained. A method has been developed for assessing the dynamic quality of metalcutting machines using a complex quality indicator determined by measuring and analyzing the spectral characteristics of vibration signals.

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Tiboni, Monica, Carlo Remino, Roberto Bussola, and Cinzia Amici. "A Review on Vibration-Based Condition Monitoring of Rotating Machinery." Applied Sciences 12, no. 3 (2022): 972. http://dx.doi.org/10.3390/app12030972.

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Monitoring vibrations in rotating machinery allows effective diagnostics, as abnormal functioning states are related to specific patterns that can be extracted from vibration signals. Extensively studied issues concern the different methodologies used for carrying out the main phases (signal measurements, pre-processing and processing, feature selection, and fault diagnosis) of a malfunction automatic diagnosis. In addition, vibration-based condition monitoring has been applied to a number of different mechanical systems or components. In this review, a systematic study of the works related to the topic was carried out. A preliminary phase involved the analysis of the publication distribution, to understand what was the interest in studying the application of the method to the various rotating machineries, to identify the interest in the investigation of the main phases of the diagnostic process, and to identify the techniques mainly used for each single phase of the process. Subsequently, the different techniques of signal processing, feature selection, and diagnosis are analyzed in detail, highlighting their effectiveness as a function of the investigated aspects and of the results obtained in the various studies. The most significant research trends, as well as the main innovations related to the various phases of vibration-based condition monitoring, emerge from the review, and the conclusions provide hints for future ideas.

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Chen, Yangbo, Maria Q. Feng, and Chin-An Tan. "Bridge Structural Condition Assessment Based on Vibration and Traffic Monitoring." Journal of Engineering Mechanics 135, no. 8 (2009): 747–58. http://dx.doi.org/10.1061/(asce)0733-9399(2009)135:8(747).

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Gierlak, Piotr, Andrzej Burghardt, Dariusz Szybicki, Marcin Szuster, and Magdalena Muszyńska. "On-line manipulator tool condition monitoring based on vibration analysis." Mechanical Systems and Signal Processing 89 (May 2017): 14–26. http://dx.doi.org/10.1016/j.ymssp.2016.08.002.

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Potočnik, Primož, and Edvard Govekar. "Semi-supervised vibration-based classification and condition monitoring of compressors." Mechanical Systems and Signal Processing 93 (September 2017): 51–65. http://dx.doi.org/10.1016/j.ymssp.2017.01.048.

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Dai, Yu, Yuan Xue, and Jianxun Zhang. "Vibration-Based Milling Condition Monitoring in Robot-Assisted Spine Surgery." IEEE/ASME Transactions on Mechatronics 20, no. 6 (2015): 3028–39. http://dx.doi.org/10.1109/tmech.2015.2414177.

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Nathan, R. J., and M. P. Norton. "Vibration Signature Based Condition Monitoring of Bowl-Roller Coal Pulverizers." Journal of Vibration and Acoustics 115, no. 4 (1993): 452–62. http://dx.doi.org/10.1115/1.2930372.

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The overall objective of the work reported in this paper is to minimize the cost of power generation in thermal power stations utilizing pulverized coal combustion processes for steam generation. The strategy of achieving this objective is based on an “on-condition maintenance” philosophy and vibration based diagnostic signature analysis techniques. The coal pulverizers reported on here are 783 RP (roll pressure) and 823 RP combustion engineering (CE) bowl-roller coal pulverizers (bowl mills) installed at the State Energy Commission of Western Australia (SECWA) power stations. This paper reviews the design philosophy, operational principles, and system dynamics and establishes the procedures for identifying the potential malfunction of bowl mills and their associated components. The influence of operating parameters, such as coal flow, primary air flow, and operating temperature, on mill vibration are investigated. The effects of journal spring force variation, such as magnitude, uneven spring force, and broken springs, are also studied. Special attention is also given to the diagnosis of the top radial bearing problem due to its remoteness from the bowl mill external structure. A spectral recovery technique utilizing the inverse frequency response function was developed for trend analysis and diagnostic purposes.

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Al-Hinai, Abdulhamid Hamdan, Karu Clement Varaprasad, and V. Vinod Kumar. "Comprehensive review of vibration-based analysis for wind turbine condition monitoring." Mechanical Engineering for Society and Industry 4, no. 3 (2024): 570–605. https://doi.org/10.31603/mesi.12466.

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Wind energy production relies heavily on the efficiency of wind turbine systems. The routine condition monitoring and maintenance of these systems are necessary to maintain healthy operation, reduce maintenance costs, minimize downtime, and extend the lifespan. Vibration based analysis is an essential technique for wind turbine condition monitoring that enables early detection of mechanical faults, abnormal behavior and degradation mechanisms, and lessens the risk of unexpected failures. This review paper explores an intensive review of various vibration based techniques of condition monitoring, their advancements, challenges, and trends. This review paper reveals that this technique of condition monitoring is effective and essential to ensure the efficiency of wind energy systems. The review paper identifies future research prospects and potential technological advancements to ensure wind energy systems' reliability, safety, and optimal performance.

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Chen, Kaikang, Bo Zhao, Yanli Zhang, et al. "Digital Twin-Based Vibration Monitoring of Plant Factory Transplanting Machine." Applied Sciences 13, no. 22 (2023): 12162. http://dx.doi.org/10.3390/app132212162.

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In response to the problem of bowl seedling detachment caused by the shaking of the transplanting machine in plant factories, this paper proposes a physical entity monitoring method for the digital twin (DT) plant factory transplanting system. The method is used to analyze the vibration signals of the transplanting machine under different operating conditions and explore the optimal working conditions. Firstly, a demand analysis for the physical entity of the DT plant factory transplanting system is conducted, focusing on practical applications. Then, an optimal deployment plan is designed based on the axiomatic design (AD) theory. Subsequently, a comparative analysis of the operating conditions of the plant factory transplanting equipment is carried out using data-driven approaches. Finally, the optimal working condition parameters are determined by comparing the modal vibration power spectral density of the transplanting equipment under different operating conditions. The results show that the maximum amplitude occurs in the Z-axis, with a magnitude of 2.1 m/s2. By comparing the cloud maps, it is evident that the vibration trends in the Z-axis and X-axis above the transplanting robotic arm are more pronounced compared to the Y-axis. This indicates that under the operating condition of transplanting 3000 plants per hour, a high transplanting efficiency can be maintained, and the vibration signals in the XYZ-axis above the transplanting robotic arm are relatively smooth, making them suitable for transplanting operations. This study combines digital twin technology to analyze the vibration signals of the plant factory transplanting machine under different operating conditions and explore the optimal working conditions. Compared to traditional monitoring platforms, this method facilitates the real-time visualization of different operating conditions of the transplanting machine in a virtual mapping, providing a more intuitive reflection of the equipment operation status.

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Huang, Xili, Bin Wei, Ziyun Ling, Fang Yang, and Hongchen Pang. "A Low-Frequency Vibration Sensor Based on Ball Triboelectric Nanogenerator for Marine Pipeline Condition Monitoring." Sensors 24, no. 12 (2024): 3817. http://dx.doi.org/10.3390/s24123817.

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Marine pipeline vibration condition monitoring is a critical and challenging issue, on account of the complex marine environment, while powering the required monitoring sensors remains problematic. This study introduces a vibration sensor based on a ball triboelectric nanogenerator (B-TENG) for marine pipelines condition monitoring. The B-TENG consists of an acrylic cube, polyester rope, aluminum electrodes, and PTFE ball, which converts vibration signals into electrical signals without the need for an external energy supply. The experimental results show that B-TENG can accurately monitor the frequency, amplitude, and direction of vibration in the range of 1–5 Hz with a small error of 0.67%, 4.4%, and 5%, and an accuracy of 0.1 Hz, 0.97 V/mm, and 1.5°, respectively. The hermetically sealed B-TENG can monitor vibration in underwater environments. Therefore, the B-TENG can be used as a cost-effective, self-powered, highly accurate vibration sensor for marine pipeline monitoring.

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Niu, Ruibin. "Mechanical Vibration Test Based on the Wireless Vibration Monitoring System." Security and Communication Networks 2022 (August 25, 2022): 1–8. http://dx.doi.org/10.1155/2022/9022128.

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In order to apply wireless sensor networks to mechanical vibration monitoring, the author proposes a wireless network topology with multiple data collection points for mechanical vibration monitoring. This structure reduces the transmission load of the data collection point, increases the data transmission rate of the network, balances the energy dissipation in the network, and utilizes the general wireless sensor network hardware platform. The network transmission protocol and related auxiliary mechanisms are designed and implemented, and a wireless vibration monitoring test platform is constructed. The transmission performance of the network structure with multiple data collection points is evaluated through the actual test. The experimental results show that by using the wireless sensor network topology with multiple data collection points, it can meet the requirements of continuous transmission of vibration data obtained by 1 kHz sampling. Conclusion. The system performance of the wireless sensor network based on this network structure has been improved under the condition of general hardware, and the network structure of multiple data collection points shows good performance in the process of high-speed data transmission.

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Kestel, Kayacan, Faras Jamil, Jens Jo Matthys, et al. "Offshore field experimentation for novel hybrid condition monitoring approaches." Journal of Physics: Conference Series 2745, no. 1 (2024): 012009. http://dx.doi.org/10.1088/1742-6596/2745/1/012009.

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Abstract This study details the development of a fully automated pipeline for the condition monitoring of wind turbine drive trains. Vibration data is collected using hardware designed and manufactured in-house and used directly to monitor the condition of the drive trains. The complex nature of wind turbine vibration signals, due to the large number of components and highly variable operating conditions, makes drive train condition monitoring a challenging task. This paper details the full data measurement and analysis flow from sensor to insights and proposes a hybrid automated pipeline with signal processing and data-driven techniques to address the complexity of dealing with wind turbine vibration data. The vibration signals are directly employed to estimate the wind turbine’s instantaneous angular speed to compensate for any rotation speed fluctuations. Pre-processing is performed on the speed-independent signals to evaluate condition indicators in both the time and spectral domain for the vibration signals and their envelopes. Machine learning is then employed to distinguish the healthy state of the machine from a faulty one using the computed condition indicators. Besides the scalar indicators, also two-dimensional vibration decompositions such as the cyclic spectral correlation maps are used as inputs to the machine learning pipeline. This comprehensive and automated approach ensures both an early and reliable fault detection. Experimental results demonstrate that the fully automated hybrid pipeline can effectively be used for fleet-based health tracking of offshore wind turbine drivetrains.

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Pookkuttath, Sathian, Povendhan Arthanaripalayam Palanisamy, and Mohan Rajesh Elara. "AI-Enabled Condition Monitoring Framework for Outdoor Mobile Robots Using 3D LiDAR Sensor." Mathematics 11, no. 16 (2023): 3594. http://dx.doi.org/10.3390/math11163594.

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An automated condition monitoring (CM) framework is essential for outdoor mobile robots to trigger prompt maintenance and corrective actions based on the level of system deterioration and outdoor uneven terrain feature states. Vibration indicates system failures and terrain abnormalities in mobile robots; hence, five vibration threshold classes for CM in outdoor mobile robots were identified, considering both vibration source system deterioration and uneven terrain. This study proposes a novel CM approach for outdoor mobile robots using a 3D LiDAR, employed here instead of its usual use as a navigation sensor, by developing an algorithm to extract the vibration-indicated data based on the point cloud, assuring low computational costs without losing vibration characteristics. The algorithm computes cuboids for two prominent clusters in every point cloud frame and sets motion points at the corners and centroid of the cuboid. The three-dimensional vector displacement of these points over consecutive point cloud frames, which corresponds to the vibration-affected clusters, are compiled as vibration indication data for each threshold class. A simply structured 1D Convolutional Neural Network (1D CNN)-based vibration threshold prediction model is proposed for fast, accurate, and real-time application. Finally, a threshold class mapping framework is developed which fuses the predicted threshold classes on the 3D occupancy map of the workspace, generating a 3D CbM map in real time, fostering a Condition-based Maintenance (CbM) strategy. The offline evaluation test results show an average accuracy of vibration threshold classes of 89.6% and consistent accuracy during real-time field case studies of 89%. The test outcomes validate that the proposed 3D-LiDAR-based CM framework is suitable for outdoor mobile robots, assuring the robot’s health and operational safety.

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Qadir, Javed, Hameed Qaiser, Mehar Ali, and Masood Iqbal. "Condition monitoring of PARR-1 rotating machines by vibration analysis technique." Nuclear Technology and Radiation Protection 29, no. 3 (2014): 249–52. http://dx.doi.org/10.2298/ntrp1403249q.

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Vibration analysis is a key tool for preventive maintenance involving the trending and analysis of machinery performance parameters to detect and identify developing problems before failure and extensive damage can occur. A lab-based experimental setup has been established for obtaining fault-free and fault condition data. After this analysis, primary and secondary motor and pump vibration data of the Pakistan Research Reactor-1 were obtained and analyzed. Vibration signatures were acquired in horizontal, vertical, and axial directions. The 48 vibration signatures have been analyzed to assess the operational status of motors and pumps. The vibration spectrum has been recorded for a 2000 Hz frequency span with a 3200 lines resolution. The data collected should be helpful in future Pakistan Research Reactor-1 condition monitoring.

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Chang, Hong-Chan, Yu-Ming Jheng, Cheng-Chien Kuo, and Yu-Min Hsueh. "Induction Motors Condition Monitoring System with Fault Diagnosis Using a Hybrid Approach." Energies 12, no. 8 (2019): 1471. http://dx.doi.org/10.3390/en12081471.

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This study develops a condition monitoring system, which includes operating condition monitoring (OCM) and fault diagnosis analysis (FDA). The OCM uses a vibration detection approach based on the ISO 10816-1 and NEMA MG-1 international standards, and the FDA uses a vibration-electrical hybrid approach based on various indices. The system can acquire real-time vibration and electrical signals. Once an abnormal vibration has been detected by using OCM, the FDA is applied to classify the type of faults. Laboratory results indicate that the OCM can successfully diagnose induction motors healthy condition, and FDA can classify the various damages stator fault, rotor fault, bearing fault and eccentric fault. The FDA with the hybrid approach is more reliable than the traditional approach using electrical detection alone. The proposed condition monitoring system can provide simple and clear maintenance information to improve the reliability of motor operations.

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Holroyd, Trevor J. "Use of AE Based Instrumentation to Monitor Machinery Condition in the Industrial Environment." Advanced Materials Research 13-14 (February 2006): 45–50. http://dx.doi.org/10.4028/www.scientific.net/amr.13-14.45.

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The use of AE by maintenance personnel for monitoring the condition of rotating machinery on the industrial shop floor is now well established and provides both a quick and effective assessment. Despite early resistance, especially by those accustomed to vibration based monitoring, it now enjoys a widespread acceptance. The development of signal processing routines and instrumentation specifically for the condition monitoring role has been a major factor in this achievement. Experience has shown that as a portable instrument AE can be very quickly applied and give instant indications of machine condition with high sensitivity to fault conditions. Appropriately pre-processed AE signals are particularly useful for on-line monitoring since the fault indications are in general less affected by changes in operating conditions than vibration based techniques as well as being far simpler to interpret. This is especially important where many machines are being simultaneously monitored. This paper discusses the accompanying developments and presents illustrative application examples.

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Gomez, María Jesús, Cristina Castejon, Eduardo Corral, and Marco Cocconcelli. "Railway Axle Early Fatigue Crack Detection through Condition Monitoring Techniques." Sensors 23, no. 13 (2023): 6143. http://dx.doi.org/10.3390/s23136143.

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The detection of cracks in rotating machinery is an unresolved issue today. In this work, a methodology for condition monitoring of railway axles is presented, based on crack detection by means of the automatic selection of patterns from the vibration signal measurement. The time waveforms were processed using the Wavelet Packet Transform, and appropriate alarm values for diagnosis were calculated automatically using non-supervised learning techniques based on Change Point Analysis algorithms. The validation was performed using vibration signals obtained during fatigue tests of two identical railway axle specimens, one of which cracked during the test while the other did not. During the test in which the axle cracked, the results show trend changes in the energy of the vibration signal associated with theoretical defect frequencies, which were particularly evident in the direction of vibration that was parallel to the track. These results are contrasted with those obtained during the test in which the fatigue limit was not exceeded, and the test therefore ended with the axle intact, verifying that the effects that were related to the crack did not appear in this case. With the results obtained, an adjusted alarm value for a condition monitoring process was established.

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Cornel, Daniel, Francisco Gutiérrez Guzmán, Georg Jacobs, and Stephan Neumann. "Condition monitoring of roller bearings using acoustic emission." Wind Energy Science 6, no. 2 (2021): 367–76. http://dx.doi.org/10.5194/wes-6-367-2021.

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Abstract. Roller bearing failures in wind turbines' gearboxes lead to long downtimes and high repair costs, which could be reduced by the implementation of a predictive maintenance strategy. In this paper and within this context, an acoustic-emission-based condition monitoring system is applied to roller bearing test rigs with the aim of identifying critical operating conditions before bearing failures occurs. Furthermore, a comparison regarding detection times is carried out with traditional vibration-based condition monitoring systems, with a focus on premature bearing failures such as white etching cracks. The investigations show a sensitivity of the acoustic-emission system towards lubricating conditions. In addition, the system has shown that a damaged surface can be detected at least ∼ 4 % (8 h, regarding the time to failure) earlier than by using the vibration-based system. Furthermore, significant deviations from the average acoustic-emission signal were detected up to ∼ 50 % (130 h) before the test stop and are possibly related to sub-surface damage initiation and might result in an earlier damage detection in the future.

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Wu, Chuan Hui, Yu Guo, and Ya Jun Fan. "Gear Vibration Monitoring System Based on Virtual Instruments." Applied Mechanics and Materials 187 (June 2012): 161–64. http://dx.doi.org/10.4028/www.scientific.net/amm.187.161.

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Vibration analysis is widely used for gear faults diagnosis. A gear vibration monitoring system based on virtual instrument developing platform LabVIEW and vibration analysis technology is developed and introduced in this paper. It satisfies the requirements of machinery condition monitoring and supports function expansion. Online and offline monitoring of gear running states can be realized though this system in both time domain, frequency domain and joint time –frequency domain. Experiments showed that this gear vibration monitoring system can be widely employed in gearbox. System not only guarantees the accuracy of the test results but also provides a friendly user interface for users’ easy operation.

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Lee Zhiyung, Joshua, Khairil Anas Md Rezali, and Azizan As’arry. "Monitoring of cooling tower water pumps using Arduino data acquisition device." Journal of Physics: Conference Series 2721, no. 1 (2024): 012018. http://dx.doi.org/10.1088/1742-6596/2721/1/012018.

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Abstract Investing equipment to monitor the condition of assets through vibration can be expensive. Recent development of alternative microcontrollers has enabled researchers to study its potential to replace expensive equipment for monitoring purposes. This study aimed to investigate the potential of alternative microcontrollers to be used as devices to monitor the condition of water pumps. An internet of things (IoT) device was developed that can measure vibration and be applied to monitor water pumps. The vibration data was obtained when the pumps were operated and sent to the condition-based monitoring system (CBMS) database via Wi-Fi network. The vibration data was observed, and current and future condition was identified through vibration analysis.

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Kim, Yeon Whan, Ju-Young Ho, and Young Shin Lee. "DEVELOPMENT OF VIBRATION CONDITION MONITORING SYSTEM APPLYING OPTICAL SENSORS FOR GENERATOR WINDING INTEGRITY OF POWER UTILITIES." International Journal of Modern Physics: Conference Series 06 (January 2012): 98–103. http://dx.doi.org/10.1142/s2010194512003005.

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This paper describes the vibration condition monitoring diagnosis system developed for stator and rotor winding integrity assessment of 100MW class gas turbine generator in combined-cycle thermal power plant. High reliability of windings is one of the most essential prerequisite for generators of power utilities. Assessing the condition of stator winding insulation systems requires objective information from condition monitoring system. In-service monitoring is essential if a power plant is following a condition-based maintenance strategy. Generator damages are caused by the high vibration and the power system instability by secondary impacts of an unannounced plant stop and the life of the generator is decreased. The mechanical vibration in generator is induced by both mechanical and magnetic forces. The vibration condition monitoring system is required for the improved savings of operation and maintenance cost in terms of reliability in power plant.

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Jenab, K., K. Rashidi, and S. Moslehpour. "An Intelligence-Based Model for Condition Monitoring Using Artificial Neural Networks." International Journal of Enterprise Information Systems 9, no. 4 (2013): 43–62. http://dx.doi.org/10.4018/ijeis.2013100104.

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This paper reports a newly developed Condition-Based Maintenance (CBM) model based on Artificial Neural Networks (ANNs) which takes into account a feature (e.g., vibration signals) from a machine to classify the condition into normal or abnormal. The model can reduce equipment downtime, production loss, and maintenance cost based on a change in equipment condition (e.g., changes in vibration, power usage, operating performance, temperatures, noise levels, chemical composition, debris content, and volume of material). The model can effectively determine the maintenance/service time that leads to a low maintenance cost in comparison to other types of maintenance strategy. Neural Networks tool (NNTool) in Matlab is used to apply the model and an illustrative example is discussed.

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Vasan, Vinod, Naveen Venkatesh Sridharan, Anoop Prabhakaranpillai Sreelatha, and Sugumaran Vaithiyanathan. "Tire Condition Monitoring Using Transfer Learning-Based Deep Neural Network Approach." Sensors 23, no. 4 (2023): 2177. http://dx.doi.org/10.3390/s23042177.

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Monitoring tire condition plays a deterministic role in the overall safety and economy of an automobile. The tire condition monitoring system (TCMS) alerts the driver of the vehicle if the inflation pressure of a particular tire decreases below a specific value. Owing to the high costs involved in realizing this system, most vehicles do not feature this technology as a standard. With highly robust and accurate sensors making their way into an increasing number of applications, obtaining signals of varied types (especially vibration signals) is becoming easier and more modularized. In addition, feature-based machine learning techniques that enable accurate responses to varied input conditions have sought greater scientific attention. However, deep learning is gradually finding greater applications pertaining to condition monitoring. One approach of deep learning is presented in this paper, which instantaneously monitors the vehicle tire condition. For this purpose, vibration signals were obtained through the rotation of the tire under different inflation pressure conditions using a low-cost microelectromechanical system (MEMS) accelerometer.

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Shan, Guang Kun, Hai Long Zhang, Xiao Dong Wang, and Ying Ming Liu. "Application of FastICA Algorithm in Wind Turbine Condition Monitoring." Applied Mechanics and Materials 217-219 (November 2012): 2750–53. http://dx.doi.org/10.4028/www.scientific.net/amm.217-219.2750.

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In wind turbine condition monitoring, the sensors often can not be installed to the ideal position. Compare the common signal processing method comprehensively and give the advantage of the fastICA algorithm in the wind turbine condition monitoring. Give the basic principle and mathematical model of the fastICA algorithm, while monitor and analysis the wind turbine state data based on the fastICA algorithm. The results show that this algorithm can separate the vibration characteristics of the tested compenent of the wind turbine from the vibration signals quickly and accurately.

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Takahashi, Yoshinori, Toru Taniguchi, and Mikio Tohyama. "Structural Condition Monitoring by Cumulative Harmonic Analysis of Random Vibration." Advances in Acoustics and Vibration 2008 (August 3, 2008): 1–8. http://dx.doi.org/10.1155/2008/261758.

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Analysis of signals based on spectral accumulation has great potential for enabling the condition of structures excited by natural forces to be monitored using random vibration records. This article describes cumulative harmonic analysis (CHA) that was achieved by introducing a spectral accumulation function into Berman and Fincham's conventional cumulative analysis, thus enabling potential new areas in cumulative analysis to be explored. CHA effectively enables system damping and modal overlap conditions to be visualized without the need for transient-vibration records. The damping and modal overlap conditions lead to a spectral distribution around dominant spectral peaks due to structural resonance. This distribution can be revealed and emphasized by CHA records of magnitude observed even within short intervals in stationary random vibration samples.

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Gao, Hong Li, Xiao Hui Shi, Ling Cong Feng, and Li Ping Xu. "Condition Monitoring and Life Prediction of Rolling Guide Based on Hybrid Intelligence." Applied Mechanics and Materials 44-47 (December 2010): 2045–49. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.2045.

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To evaluate accurately working condition of guide, make maintenance strategy, and predict its residual life in the process of machining operation, a rolling guide rail condition monitoring system based on neural networks was constructed after key factors to guide life were investigated carefully. Eight B&K 4321 three-way vibration sensor were installed on slider surface to monitor the on-line condition of four guides and eight sliders. Vibration signals were processed by wavelet packet decomposition and the most sensitive features to guide life were selected by fuzzy clustering method. The relation between guide life and input vectors including vibration features and machining condition was built by radial basis probabilistic neural networks (RBPNN), which parameters were optimized by genetic algorithm. The experimental results show maximum forecast error is 360 hours and minimum forecast error is 63 hours.

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Mohammed, Ouali, and Magraoui Rabah. "Contribution to Conditional Maintenance by Vibration Analysis of Rotating Machine Mechanical Failures and Proposed Solutions." Revista de Gestão Social e Ambiental 18, no. 10 (2024): e08541. http://dx.doi.org/10.24857/rgsa.v18n10-052.

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Objective: The aim of this study is to analyze the Cement Separator machine and identify potential condition indicators through vibration measurements and analysis. The goal is to diagnose and repair the machine before any material damage occurs. Background: Maintenance Technology: Modern maintenance technologies are crucial for monitoring equipment performance and planning timely maintenance interventions. Vibration Monitoring: As a key component in maintenance programs, especially for mechanical systems, vibration monitoring provides early warnings of faults based on the actual state of the machine. Cement Separator Machine: Issue: The cement separator exhibits significant vibrations despite its low operating speed. Data Sources: The construction and operational details of the machine are available from its operating manual and vibration measurements. Methodology: Practical Vibration Measurements: Extensive measurements are performed in a research laboratory and on-site to monitor the machine's condition. Vibration measuring devices are used to monitor rotating machines at specified intervals according to their condition and international standards. Condition Indicators: Various condition indicators are monitored to detect possible deterioration and the onset of mechanical or electrical failures during the machine's operation. Experimental Studies: Practical experiments validate the theoretical and numerical findings and refine the vibration analysis approach. Strategy: Adequate scheduling of interventions and repairs based on the machine's condition. Continuous monitoring through vibration analysis devices. Expected Outcomes: Early detection of potential mechanical or electrical failures. Sustainable solutions to reduce vibrations and enhance the machine's performance. Improved maintenance scheduling to prevent material damage and extend the machine's lifespan. By integrating vibration monitoring and analysis with practical studies, this research aims to provide a comprehensive diagnosis and repair strategy for the cement separator machine.

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Wan, Yu, Shaochen Lin, and Yan Gao. "Pipeline and Rotating Pump Condition Monitoring Based on Sound Vibration Feature-Level Fusion." Machines 12, no. 12 (2024): 921. https://doi.org/10.3390/machines12120921.

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The rotating pump of pipelines are susceptible to damage based on extended operations in a complex environment of high temperature and high pressure, which leads to abnormal vibrations and noises. Currently, the method for detecting the conditions of pipelines and rotating pumps primarily involves identifying their abnormal sounds and vibrations. Due to complex background noise, the performance of condition monitoring is unsatisfactory. To overcome this issue, a pipeline and rotating pump condition monitoring method is proposed by extracting and fusing sound and vibration features in different ways. Firstly, a hand-crafted feature set is established from two aspects of sound and vibration. Moreover, a convolutional neural network (CNN)-derived feature set is established based on a one-dimensional CNN (1D CNN). For the hand-crafted and CNN-derived feature sets, a feature selection method is presented for significant features by ranking features according to their importance, which is calculated by ReliefF and the random forest score. Finally, pipeline and rotating pump condition monitoring is applied by fusing the significant sound and vibration features at the feature level. According to the sound and vibration signals obtained from the experimental platform, the proposed method was evaluated, showing an average accuracy of 93.27% for different conditions. The effectiveness and superiority of the proposed method are manifested through comparison and ablation experiments.

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Gnanasekaran, Sakthivel, Lakshmipathi Jakkamputi, Mohanraj Thangamuthu, et al. "Condition Monitoring of an All-Terrain Vehicle Gear Train Assembly Using Deep Learning Algorithms with Vibration Signals." Applied Sciences 12, no. 21 (2022): 10917. http://dx.doi.org/10.3390/app122110917.

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Condition monitoring of gear train assembly has been carried out with vibration signals acquired from an all-terrain vehicle (ATV) gearbox. The location of the defect in the gear was identified based on finite element analysis results. The vibration signals were acquired using an accelerometer under good and simulated fault conditions of the gear. The raw vibration signatures acquired from all the possible conditions of the gear train assembly were processed using the descriptive statistics tool. A set of descriptive statistical features were extracted from the raw vibrational signals. This study used a deep learning algorithm based on the tree family, which includes the decision tree, random forest, and random tree algorithms, to classify gear train conditions. Among the tree family algorithms, the random forest algorithm produced maximum classification accuracy of 99%. The decision rules were used to design an online monitoring system to display the gear condition. This study will help to implement online gear health monitoring in ATVs, ensuring the safety of drivers.

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Gomathi, K., and A. Balaji. "Tool condition monitoring of PCB milling machine based on vibration analysis." Materials Today: Proceedings 45 (2021): 3386–97. http://dx.doi.org/10.1016/j.matpr.2020.12.778.

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Ansari, S. A., and R. Baig. "A PC-based vibration analyzer for condition monitoring of process machinery." IEEE Transactions on Instrumentation and Measurement 47, no. 2 (1998): 378–83. http://dx.doi.org/10.1109/19.744177.

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