International Journal of Prognostics and Health Management, ISSN 2153-2648, 2017 019 1 A Condition Based Maintenance Implementation for an Automated People Mover Gearbox
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Published
Nov 17, 2020
Ali Ashasi-Sorkhabi
Stanley Fong
Guru Prakash
Sriram Narasimhan
Abstract
Data-driven condition-based maintenance (CBM) can be an effective predictive maintenance strategy for components within complex systems with unknown dynamics, nonstationary vibration signatures or a lack of historical failure data. CBM strategies allow operators to maintain components based on their condition in lieu of traditional alternatives such as preventive or corrective strategies. In this paper, the authors present an outline of the CBM program and a field pilot study being conducted on the gearbox, a critical component in an automated cable-driven people mover (APM) system at Toronto’s Pearson airport. This CBM program utilizes a paired server-client “two-tier” configuration for fault detection and prognosis. At the first level, fault detection is performed in real-time using vibration data collected from accelerometers mounted on the APM gearbox. Time-domain condition indicators are extracted from the signals to establish the baseline condition of the system to detect faults in real-time. All tier one tasks are handled autonomously using a controller located on-site. In the second level pertaining to prognostics, these condition indicators are utilized for degradation modeling and subsequent remaining useful life (RUL) estimation using random coefficient and stochastic degradation models. Parameter estimation is undertaken using a hierarchical Bayesian approach. Degradation parameters and the RUL model are updated in a feedback loop using the collected degradation data. While the case study presented will primarily focus on a cable-driven APM gearbox, the underlying theory and the tools developed to undertake diagnostics and prognostics tasks are broadly applicable to a wide range of other civil and industrial applications.
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Keywords
Bayesian inference, Condition Based Maintenance, bearing diagnostics, APM transit
References
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Ali, J. Ben, Chebel-morello, B., Saidi, L., & Malinowski, S. (2014). Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network. Mechanical Systems and Signal Processing, 1–23. http://doi.org/10.1016/j.ymssp.2014.10.014
Baum, M., Gheţa, I., Belkin, A., Beyerer, J., & Hanebeck, U. D. (2017). Data association in a world model for autonomous systems. In IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (pp. 187–192). Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-78649315410&partnerID=40&md5=b64018bbc8d668cea4afa18356d89a75
Bechhoefer, E., Schlanbusch, R., & Waag, T. I. (2016). Techniques for Large, Slow Bearing Fault Detection. International Journal of Prognostics and Health Management, 2153–2648.
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. http://doi.org/10.1016/j.ymssp.2011.01.013
Bunks, C., & Mccarthy, D. (2000). Condition-Based Maintenance of Machines Using Hidden Markov Models. Mechanical Systems and Signal Processing, 14(4), 597–612. http://doi.org/10.1006
Chen, N., & Tsui, K. L. (2013). Condition monitoring and remaining useful life prediction using degradation signals: revisited. IIE Transactions, 45(9), 939–952. http://doi.org/10.1080/0740817X.2012.706376
Chin, S. C., Ray, A., & Rajagopalan, V. (2005). Symbolic time series analysis for anomaly detection: A comparative evaluation. Signal Processing, 85(9), 1859–1868. http://doi.org/10.1016/j.sigpro.2005.03.014
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Series B Stat Methodol (Vol. 39).
Doppelmayr Cable Car. (2017). Cable-propelled automated people mover systems. Retrieved from https://www.dcc.at/solutions/system-features/
Ellingwood, B., & Yasuhiro, M. (1993). Probabilistic methods for condition assessment and life prediction of concrete structures in nuclear power plants. Nuclear Engineering and Design, 142, 155–166. http://doi.org/10.1016/0029-5493(93)90199-J
Fugate, Michael L and Sohn, Hoon and Farrar, C. R. (2001). Vibration-based damage detection using statistical process control. Mechanical Systems and Signal Processing, 15, 707–721.
Jardine, A. K. S., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510. http://doi.org/10.1016/j.ymssp.2005.09.012
Kaiser, K. A., & Gebraeel, N. Z. (2009). Predictive maintenance management using sensor-based degradation models. IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, 39(4), 840–849. http://doi.org/10.1109/TSMCA.2009.2016429
Kumar, U., & Klefsjo, B. (1992). Reliability analysis of hydraulic systems of LHD machines using the power law process model. Reliability Engineering & System Safety, 35, 217–224. http://doi.org/10.1016/0951-8320(92)90080-5
Lee, M. D., Houssein, M. A. E., & Shahidul, M. I. (2016). Production Machinery Maintenance Cost Optimization : A Review. International Journal of Advanced Engineering Research and Application, 2(3), 131–146.
Liu, B., & Makis, V. (2008). Gearbox failure diagnosis based on vector autoregressive modelling of vibration data and dynamic principal component analysis. IMA Journal of Management Mathematics, 19(1), 39–50. http://doi.org/10.1093/imaman/dpm002
Liu, Q., Dong, M., & Peng, Y. (2012). A novel method for online health prognosis of equipment based on hidden semi-Markov model using sequential Monte Carlo methods. Mechanical Systems and Signal Processing, 32, 331–348. http://doi.org/10.1016/j.ymssp.2012.05.004
Lu, C. J., & Meeker, W. Q. (1993). Using Degradation Measures to Estimate a Time-to-Failure Distribution. Technometrics, 35(2), 161. http://doi.org/10.2307/1269661
Meeker, W. Q., & Escobar, A. L. (2014). Statistical Methods for Reliability Data. John Wiley & Sons, Inc.
Montgomery, D. (2009). Introduction to statistical quality control. John Wiley & Sons Inc. http://doi.org/10.1002/1521-3773(20010316)40:6<9823::AIDANIE9823> 3.3.CO;2-C
Nelwamondo, F., Marwala, T., & Mahola, U. (2006). Early Classifications of Bearing Faults Using Hidden. Information and Control, 2(6), 1281–1299. NI 9234 User Guide and Specifications. (2014). Retrieved from http://www.ni.com/datasheet/pdf/en/ds-316NI 9263 User Guide and Specifications. (2014), (866), 1–10. Retrieved from http://www.ni.com/datasheet/pdf/en/ds-59
Rabiner, L. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257–286. http://doi.org/10.1109/5.18626
Randall, R. B. (2012). Vibration-based Condition Monitoring. John Wiley & Sons, Inc.
Randall, R. B., & Antoni, J. (2011). Rolling element bearing diagnostics-A tutorial. Mechanical Systems and Signal Processing, 25(2), 485–520. http://doi.org/10.1016/j.ymssp.2010.07.017
Sadhu, A., Prakash, G., & Narasimhan, S. (2016). A hybrid hidden Markov model towards fault detection of rotating components. Journal of Vibration and Control, (August 2015). http://doi.org/10.1177/1077546315627934
Sánchez-Silva, Mauricio and Klutke, G.-A. (2016). Degradation: Data Analysis and Analytical Modeling. zn Reliability and Life-Cycle Analysis of Deteriorating Systems (pp. 79--116). Springer.
Sharma, A., Amarnath, M., & Kankar, P. (2014). Feature extraction and fault severity classification in ball bearings. Journal of Vibration and Control, (February), 1–17. http://doi.org/10.1177/1077546314528021
Si, X. S., Wang, W., Hu, C. H., & Zhou, D. H. (2011). Remaining useful life estimation - A review on the statistical data driven approaches. European Journal of Operational Research, 213(1), 1–14. http://doi.org/10.1016/j.ejor.2010.11.018
Timusk, M., Lipsett, M., & Mechefske, C. K. (2008). Fault detection using transient machine signals. Mechanical Systems and Signal Processing, 22(7), 1724–1749. http://doi.org/10.1016/j.ymssp.2008.01.013
van Noortwijk, J. M. (2009). A survey of the application of gamma processes in maintenance. Reliability Engineering and System Safety, 94(1), 2–21. http://doi.org/10.1016/j.ress.2007.03.019
Večeř, P., Kreidl, M., & Šmíd, R. (2005). Condition Indicators for Gearbox Condition Monitoring Systems. Acta Polytechnica, 45(6), 35–43. http://doi.org/10.14311/782
Wang, W., & Zhang, W. (2008). Early defect identification: application of statistical process control methods. Journal of Quality in Maintenance Engineering, 14(3), 225–236. http://doi.org/10.1108/13552510810899445
Wang, X., Jiang, P., Guo, B., & Cheng, Z. (2014). Real-time reliability evaluation with a general Wiener processbased degradation model. Quality and Reliability Engineering International, 30(2), 205–220. http://doi.org/10.1002/qre.1489
Whitmore, G. a, & Schenkelberg, F. (1997). Modelling accelerated degradation data using Wiener diffusion with a time scale transformation. Lifetime Data Analysis, 3(1), 27–45. http://doi.org/10.1023/A:1009664101413
Yin, S., Wang, G., & Karimi, H. R. (2014). Data-driven design of robust fault detection system for wind turbines. Mechatronics, 24(4), 298–306. http://doi.org/10.1016/j.mechatronics.2013.11.009
Zhou, W., Habetler, T. G., & Harley, R. G. (2008). Bearing fault detection via stator current noise cancellation and statistical control. IEEE Transactions on Industrial Electronics, 55(12), 4260–4269. http://doi.org/10.1109/TIE.2008.2005018
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Technical Papers