Condition Based Maintenance of Low Speed Rolling Element Bearings using Hidden Markov Model

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Published Nov 16, 2020
G. Prakash S. Narasimhan M. D. Pandey

Abstract

This paper presents an integrated hidden Markov model (HMM) approach to undertake fault diagnosis and maintenance planning for low-speed roller element bearings in a conveyor system. The components studied are relatively long-life components for which run-to-failure data is not available. Furthermore, the large number of these components in a conveyor system makes the individual monitoring of each bearing impractical. In this paper, HMM is employed to overcome both these challenges. For fault diagnosis, a number of bearings varying in age and usage were extracted from the system and tested to develop a baseline HMM model. This data was then used to calculate likelihood probabilities, which were subsequently used to determine the health state of an unknown bearing. For maintenance planning, experimentally determined thresholds from faulty bearings were used in conjunction with simulated degradation paths to parametrize a HMM. This HMM is then used to determine the state duration statistics and subsequently the calculation of residual useful life (RUL) based on bearing vibration data. The RUL distribution is then used for maintenance planning by optimizing the expected cost rate and the results so obtained are compared with the results obtained from a traditional age based replacement policy.

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Keywords

CBM, prognostics, Machinery diagnostics, roller bearings

References
Alshraideh, H., & Runger, G. (2014). Process monitoring using hidden markov models. Quality and Reliability Engineering International, 30(8), 1379–1387.
Asadoorian, M. O., & Kantarelis, D. (2005). Essentials of inferential statistics. University Press of America.
Barlow, R., & Hunter, L. (1960). Optimum preventive maintenance policies. Operations Research, 8(1), 90–100.
Baruah, P., & Chinnam, R. B. (2005). HMMs for diagnostics and prognostics in machining processes. International Journal of Production Research, 43(6), 1275–1293.
Bechhoefer, E., Bernhard, A., He, D., & Banerjee, P. (2006). Use of hidden semi-markov models in the prognostics of shaft failure. In Anuual forum proceedings american helicopter society (Vol. 62, p. 1330).
Boutros, T., & Liang, M. (2011). Detection and diagnosis of bearing and cutting tool faults using hidden markov models. Mechanical Systems and Signal Processing, 25(6), 2102–2124.
Bunks, C., McCarthy, D., & Al-Ani, T. (2000). Conditionbased maintenance of machines using hidden markov models. Mechanical Systems and Signal Processing, 14(4), 597–612.
Chen, Z., Yang, Y., Hu, Z., & Ge, Z. (2011). A new method of bearing fault diagnostics in complex rotating machines using multi-sensor mixtured hidden markov models. In Proceedings of annual conference of the prognostics and health man-agement society (pp. 1–6).
Chinnam, R. B., & Baruah, P. (2009). Autonomous diagnostics and prognostics in machining processes through competitive learning-driven hmm-based clustering. International Journal of Production Research, 47(23), 6739–6758.
Dawid, R., McMillan, D., & Revie, M. (2015). Review of markov models for maintenance optimization in the context of offshore wind. , 1–11. Dodge. (n.d.). The dodge bearings website. http://www.dodge-pt.com/products/bearing/bearinghome/. ([10-June-2016])
Dong, M., & He, D. (2007). A segmental hidden semimarkov model (hsmm)-based diagnostics and prognostics framework and methodology. Mechanical Systems and Signal Processing, 21(5), 2248–2266.
Duda, R. O., Hart, P. E., & Stork, D. G. (2000). Pattern classification (2nd edition). Wiley-Interscience.
Eker, O. F., & Camci, F. (2013). State-based prognostics with state duration information. Quality and Reliability Engineering International, 29(4), 465–476.
Elwany, A. H., & Gebraeel, N. Z. (2008). Sensor-driven prognostic models for equipment replacement and spare parts inventory. IIE Transactions, 40(7), 629–639.
Ertunc, H. M., Loparo, K. A., & Ocak, H. (2001). Tool wear condition monitoring in drilling operations using hidden markov models (HMMs). International Journal of Machine Tools and Manufacture, 41(9), 1363–1384.
Gebraeel, N. Z., Lawley, M. A., Li, R., & Ryan, J. K. (2005). Residual-life distributions from component degradation signals: A bayesian approach. IIE Transactions, 37(6), 543–557.
Jardine, A. K., & Tsang, A. H. (2013). Maintenance, replacement, and reliability: theory and applications. CRC press.
Lee, J. M., Kim, S.-J., Hwang, Y., & Song, C.-S. (2004). Diagnosis of mechanical fault signals using continuous hidden markov model. Journal of Sound and Vibration, 276(3), 1065–1080.
Lee, S., Li, L., & Ni, J. (2010). Online degradation assessment and adaptive fault detection using modified hidden markov model. Journal of Manufacturing Science and Engineering, 132(2), 021010.
Lu, C. J., & Meeker, W. O. (1993). Using degradation measures to estimate a time-to-failure distribution. Technometrics, 35(2), 161–174.
Medjaher, K., Tobon-Mejia, D. A., & Zerhouni, N. (2012). Remaining useful life estimation of critical components with application to bearings. Reliability, IEEE Transactions on, 61(2), 292–302.
Mehrabi, M. G., & Kannatey-Asibu Jr, E. (2002). Hidden markov model-based tool wear monitoring in turning. Journal of Manufacturing Science and Engineering, 124(3), 651–658.
Nelwamondo, F. V., Marwala, T., & Mahola, U. (2006). Early classifications of bearing faults using hidden markov models, gaussian mixture models, mel frequency cepstral coefficients and fractals. International Journal of Innovative Computing, Information and Control, 2(6), 1281–1299.
Ocak, H., Loparo, K., et al. (2001). A new bearing fault detection and diagnosis scheme based on hidden markov modeling of vibration signals. In Acoustics, speech, and signal processing, 2001. proceedings.(icassp’01). 2001 ieee international conference on (Vol. 5, pp. 3141–3144).
Ocak, H., & Loparo, K. A. (2005). HMM-based fault detection and diagnosis scheme for rolling element bearings. Journal of Vibration and Acoustics, 127(4), 299–306.
Ocak, H., Loparo, K. A., & Discenzo, F. M. (2007). Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling: A method for bearing prognostics. Journal of sound and vibration, 302(4), 951–961.
Peng, Y., & Dong, M. (2011). A prognosis method using age-dependent hidden semi-markov model for equipment health prediction. Mechanical Systems and Signal Processing, 25(1), 237–252.
Purushotham, V., Narayanan, S., & Prasad, S. A. (2005). Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden markov model based fault recognition. Ndt & E International, 38(8), 654–664.
Rabiner, L., & Juang, B.-H. (1986). An introduction to hidden markov models. ASSP Magazine, IEEE, 3(1), 4–16.
Rabiner, L. R., & Juang, B.-H. (1993). Fundamentals of speech recognition (Vol. 14). PTR Prentice Hall Englewood Cliffs.
Sadhu, A., Prakash, G., & Narasimhan, S. (2016). A hybrid hidden markov model towards fault detection of rotating components. Journal of Vibration and Control, 1077546315627934.
Su, C., & Shen, J. (2013). A novel multi-hidden semi-markov model for degradation state identification and remaining useful life estimation. Quality and Reliability Engineering International, 29(8), 1181–1192.
Tobon-Mejia, D., Medjaher, K., Zerhouni, N., & Tripot, G. (2011). Hidden markov models for failure diagnostic and prognostic. In Prognostics and system health management conference (phm-shenzhen), 2011 (pp. 1–8).
Van Noortwijk, J. (2009). A survey of the application of gamma processes in maintenance. Reliability Engineering & System Safety, 94(1), 2–21.
Veˇceˇr, P., Kreidl, M., & ˇSm´ıd, R. (2005). Condition indicators for gearbox condition monitoring systems. Acta Polytechnica, 45(6).
Wu, B., Tian, Z., & Chen, M. (2013). Condition-based maintenance optimization using neural network-based health condition prediction. Quality and Reliability Engineering International, 29(8), 1151–1163.
Zhang, B., Zhang, L., & Xu, J. (2016). Degradation feature selection for remaining useful life prediction of rolling element bearings. Quality and Reliability Engineering International, 32(2), 547–554.
Zhang, X., Xu, R., Kwan, C., Liang, S. Y., Xie, Q., & Haynes, L. (2005). An integrated approach to bearing fault diagnostics and prognostics. In American control conference, 2005. proceedings of the 2005 (pp. 2750–2755).
Section
Technical Papers