PHM Survey : Implementation of Signal Processing Methods for Monitoring Bearings and Gearboxes
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Published
Nov 20, 2020
Abdenour Soualhi
Yasmine Hawwari
Kamal Medjaher
Guy Clerc
Razik Hubert
François Guillet
Abstract
The reliability and safety of industrial equipments are one of the main objectives of companies to remain competitive in sectors that are more and more exigent in terms of cost and security. Thus, an unexpected shutdown can lead to physical injury as well as economic consequences. This paper aims to show the emergence of the Prognostics and Health Management (PHM) concept in the industry and to describe how it comes to complement the different maintenance strategies. It describes the benefits to be expected by the implementation of signal processing, diagnostic and prognostic methods in health-monitoring. More specifically, this paper provides a state of the art of existing signal processing techniques that can be used in the PHM strategy. This paper allows showing the diversity of possible techniques and choosing among them the one that will define a framework for industrials to monitor sensitive components like bearings and gearboxes.
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Keywords
diagnosis, prognosis, fault-tolerant control, reconfigurable control, PHM
References
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Elghazel, W., Bahi, J., Guyeux, C., Hakem, M., Medjaher, K., and Zerhouni, N. (2015). Dependability of wireless sensor networks for industrial prognostics and health management. Computers in Industry, 68:1 – 15.
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Feng, K., Wang, K., Zhang, M., Ni, Q., & Zuo, M. J. (2017). A diagnostic signal selection scheme for planetary gearbox vibration monitoring under non-stationary operational conditions. Measurement Science and Technology, 28(3), 035003.
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Giurgiutiu, Victor, and Yu, L. (2003). In Comparison of Short-time Fourier Transform and Wavelet Transform of Transient and Tone Burst Wave Propagation Signals For Structural Health Monitoring, pages 1267–1274.
Gong, X. and Qiao, W. (2013). Bearing fault diagnosis for direct-drive wind turbines via current-demodulated signals. IEEE Transactions on Industrial Electronics, 60(8):3419–3428.
Hammond, J. and White, P. (1996). The analysis of nonstationary signals using time-frequency methods. Journal of Sound and Vibration, 190(3):419 – 447.
Han, J., Dong, F., and Xu, Y. Y. (2009). Entropy feature extraction on flow pattern of gas/liquid two-phase flow based on cross-section measurement. Journal of Physics: Conference Series, 147(1):012041.
Hemmati, F., Orfali, W., and Gadala, M. S. (2016). Roller bearing acoustic signature extraction by wavelet packet transform, applications in fault detection and size estimation. Applied Acoustics, 104:101 – 118.
Heng, A., Zhang, S., Tan, A. C., and Mathew, J. (2009). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 23(3):724 – 739.
Holmberg, K., Adgar, A., Arnaiz, A., Jantunen, E., Mascolo, J., and Mekid, S. (2010). E-maintenance. Springer Publishing Company, Incorporated, 1st edition.
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Hong, L. and Dhupia, J. S. (2014). A time domain approach to diagnose gearbox fault based on measured vibration signals. Journal of Sound and Vibration, 333(7):2164 –2180.
Hountalas, D. T. (2000). Prediction of marine diesel engine performance under fault conditions. Applied Thermal Engineering, 20(18):1753 – 1783.
Jaloretto, M. R., de Oliveira, C. R. E., and Kawakami, R. (2009). Trend analysis for prognostics and health monitoring. In CTA-DLR Workshop on Data Analysis & Flight Contr, pages 14–16.
Kalgren, P. W., Byington, C. S., Roemer, M. J., and Watson, M. J. (2006). Defining phm, a lexical evolution of maintenance and logistics. In IEEE Autotestcon, pages 353–358.
Kankar, P., Sharma, S. C., and Harsha, S. (2011). Rolling element bearing fault diagnosis using wavelet transform. Neurocomputing, 74(10):1638 – 1645.
Kar, C. and Mohanty, A. (2006). Monitoring gear vibrations through motor current signature analysis and wavelet transform. Mechanical Systems and Signal Processing, 20(1):158 – 187.
Kim, B., Lee, S., Lee, M., Ni, J., Song, J., and Lee, C. (2007). A comparative study on damage detection in speed-up and coast-down process of grinding spindletyped rotor-bearing system. Journal of Materials Processing Technology, 187-188:30 – 36.
Kim, N. H., An, D., & Choi, J. H. (2017). Prognostics and health management of engineering systems. Switzerland: Springer International Publishing.
Kingsbury, N. (1998). The dual-tree complex wavelet transform: A new technique for shift invariance and directional filters. 8th IEEE DSP Workshop, pages 319–322.
Kumar, R. and Singh, M. (2013). Outer race defect width measurement in taper roller bearing using discrete wavelet transform of vibration signal. Measurement, 46(1):537 – 545.
Kumar, S., Dolev, E., and Pecht, M. (2010). Parameter selection for health monitoring of electronic products. Microelectronics Reliability, 50(2):161 – 168.
Laerhoven, K. V., Aidoo, K. A., and Lowette, S. (2001). Real-time analysis of data from many sensors with neural networks. In Proceedings Fifth International Symposium on Wearable Computers, pages 115–122.
Lee, J.-Y. (2013). Sound and vibration signal analysis using improved short-time fourier representation. International Journal of Automotive and Mechanical Engineering, 7:811–819.
Lei, Y., Lin, J., He, Z., and Zuo, M. J. (2013). A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, 35(1):108 – 126.
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Liu, B., Ling, S., and Gribonval, R. (2002). Bearing failure detection using matching pursuit. NDT & E International, 35(4):255 – 262.
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