Device Health Status Assessment Under the Influence of Multiple Exception Modes

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Published Aug 17, 2023
Xue-feng YUAN Fei-long Liu Yong-jun QIE Shuai SUN Jie REN

Abstract

Equipment reliability is the key feature to ensure the equipment operation for a long time. It is difficult to determine the overall reliability of industrial equipment due to the different reliability states of different subsystems. A device abnormality identification method based on JS (Jenson's Shannon) divergence and a health status assessment technology based on FMECA (failure mode, effect and criticality analysis) are proposed. This method enables an accurate assessment of the current health status of the device. First, the historical operation data is preprocessed according to the characteristics of the equipment to improve the data quality. The JS divergence method is reused to extract the similarity between the key feature data distribution and the benchmark data distribution. Then, the FMECA report is established using the real running data of the device combined with expert experience. Gray theory was used to determine the degree of association between one-way health state membership vector and different health state rank vector. Finally, the health status level was comprehensively evaluated by the fuzzy membership method. Taking the mechanical arm component of a 100-ton crane as an example, the results show that this method can effectively evaluate the current health state of the equipment, and provide power for the abnormal advance disposal and auxiliary management decisions.

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Keywords

Industrial equipment, Distribution similarity, FMECA, Fuzzy membership degree

References
Bencheikh, F.Harkat, M. F.Kouadri, A.Bensmail, A. (2020). New reduced kernel pca for fault detection and diagnosis in cement rotary kiln. Chemometrics and Intelligent Laboratory Systems, 204(1).DOI:10.1016/j.chemolab.2020.104091
Bhat D, Muench S, Roellig M(2023).Application of machine learning algorithms in prognostics and health monitoring of electronic systems: A review. e-Prime - Advances in Electrical Engineering, Electronics and Energy. Vol 4,2023,100166,https://doi.org/10.1016/j.prime.2023.100166.
Goncalves, J. P. S. , Fruett, F. , Dalfre Filho, J. G. , & Giesbrecht, M. . (2021). Faults detection and classification in a centrifugal pump from vibration data using markov parameters. Mechanical Systems & SignalProcessing(158),DOI:158.10.1016/j.ymssp.2021.107694
Guo S. (2014). Diagnosis of structural damage based on JS divergence. Harbin Institute of Technology. DOI:10.7666/d.D752859
Guo S. Diagnosis of structural damage based on JS divergence. Harbin Institute of Technology, 2014.
Hamadouche, A. , Kouadri, A. , & Bensmail, A. . (2018). Kernelized relative entropy for direct fault detection in industrial rotary kilns. International Journal of Adaptive Control and Signal Processing, 32(7).DOI:10.1002/acs.2879
Hu C H, Shi Q, & Si X S, et al. (2017). Progress in data-driven life span prediction and health management technologies. Progress in data-driven life span prediction and health management technologies, 46(1), 11.doi: 10.13976/j.cnki.xk.2017.0072
Pan F, Shen J X, Gao Y M, & Yang X Q. Research on engineering equipment fault prediction and health management technology.Mechanical Management Development,2021,36(05):98-100.DOI:10.16525/j.cnki.cn14-1134/th.2021.05.042.
Priyom G, Rajiv N R(2023).A systematic review on failure modes and proposed methodology to artificially seed faults for promoting PHM studies in laboratory environment for an industrial gearbox. Engineering Failure Analysis, Vol 146, 2023,107076,https://doi.org/10.1016/j.engfailanal.2023.107076.
Qin P. Functional reliability and health assessment of an explosive logic network system based on failure analysis.
(Doctoral dissertation, University of Electronic Science and Technology of China). DOI:CNKI:CDMD:2.1016.172252
Tan H Z.(2021). Application of fault prediction and health management technology in the civilian field. Mechanical manufacturing, 59(6), 6.
Wang, W. , Lai, Y. C. , & Grebogi, C. . (2016). Data based identification and prediction of nonlinear and complex dynamical systems. Physics Reports, 644, 1-76. DOI:10.1016/j.physrep.2016.06.004
Yu Y, Si X S, Hu C H, Cui M Z, & Li H P. (2018). Progress in data-driven reliability assessment and life span prediction: Covariate-based methods. Journal of Automation, 44(2), 12. DOI:10.16383/j.aas.2018.c170005
Yuan Xuefeng, Chen Mubin, Ma Chenglong, & Zhou Yanwei.(2021).Study on Multi-Sensor Parameter Warning Based on Gaussian Mixed Model and Nonlinear State Estimation Technique. 2021 IEEE Sustainable Power and Energy Conference. DOI:10.1109/iSPEC53008.2021.9735467
Zhang Xujin, Zhang Yun, Research on fault extraction of rotary kiln barrel based on wavelet packet decomposition, Mech. Eng. 09 (2021) 64–67. DOI:10.17583/ as0045
Zhang Y, Han G W, Lu N Y, Jiang B, & Zhi Y R. (2017). Health status assessment method of rail vehicle door system based on JS divergence. Mechanical Design and Manufacturing Engineering, 46(11), 6.
Zhao K. Study on failure mode and risk assessment of boiler bearing parts. (Doctoral dissertation, Shandong University).DOI:CNKI:CDMD:2.1015.531457
Zhong Xianping, Zhang Lin, Ban Heng. (2023).Deep reinforcement learning for class imbalance fault diagnosis of equipment in nuclear power plants, Annals of Nuclear Energy,184.DOI:10.1016/j.anucene.2023.109685
Zou X Y , Tao L F, Sun L L, et al.(2023).A case-learning-based paradigm for quantitative recommendation of fault diagnosis algorithms: A case study of gearbox.Reliability Engineering & System Safety.Vol 237,2023,109372,https://doi.org/10.1016/j.ress.2023.109372.
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Technical Papers