PHM Survey : Implementation of Signal Processing Methods for Monitoring Bearings and Gearboxes

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

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.

Abstract 237 | PDF Downloads 210

##plugins.themes.bootstrap3.article.details##

Keywords

diagnosis, prognosis, fault-tolerant control, reconfigurable control, PHM

References
Antoni, Jerome & Randall, R.B. (2002). Differential Diagnosis of Gear and Bearing Faults. Journal of Vibration and Acoustics. 124(2): 165-171.
Ali, J. B., Fnaiech, N., Saidi, L., Chebel-Morello, B., and Fnaiech, F. (2015). Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Applied Acoustics, 89:16 – 27.
Appleby, M. P. (2003). Wear debris detection and oil analysis using ultrasonic and capacitance measurements. Master’s thesis, Master of Science Thesis, the Graduate Faculty of the University of Akron.
Auger, F. and Flandrin, P. (1996). Ctime-frequency toolbox. CNRS France-Rice University, 46 (1996).
Averbuch, A. Z. and Zheludev, V. A. (2002). Lifting scheme for biorthogonal multiwavelets originated from hermite splines. IEEE Transactions on Signal Processing, 50(3):487–500.
Barszcz, T. and Jablonski, A. (2011). A novel method for the optimal band selection for vibration signal demodulation and comparison with the kurtogram. Mechanical Systems and Signal Processing, 25(1):431 – 451.
Bellini, A., Immovilli, F., Rubini, R., and Tassoni, C. (2008). Diagnosis of bearing faults of induction machines by vibration or current signals: A critical comparison. In IEEE Industry Applications Society Annual Meeting, pages 1–8.
Bengtsson, M. (2003). Standardization issues in condition based maintenance. In Condition Monitoring and Diagnostic Engineering Management, pages 651–660.
Bennett, W. R. (1958). Statistics of regenerative digital transmission. Bell System Technical Journal, 37(6):1501–1542.
Benkedjouh, T., Zerhouni, N., & Rechak, S. (2018). Tool wear condition monitoring based on continuous wavelet transform and blind source separation. The International Journal of Advanced Manufacturing Technology, 1-13.
Bleakie, A., & Djurdjanovic, D. (2013). Feature extraction, condition monitoring, and fault modeling in semiconductor manufacturing systems. Computers in Industry, 64(3), 203-213.
Bonnardot, F. (2004). Comparison between angular and time domain analysis of rotating machine vibratory signals. Study of fuzzy-cyclostationnarity concept. PhD thesis, Institut National Polytechnique de Grenoble - INPG.
Burgess, L. and Shimbel, T. (1995). What is the prognosis on your maintenance program. In Engineering and Mining Journal, volume 196, pages 32–35.
Cao, H., Fan, F., Zhou, K., and He, Z. (2016). Wheelbearing fault diagnosis of trains using empirical wavelet transform. Measurement, 82:439 – 449.
Casoli, P., Bedotti, A., Campanini, F., & Pastori, M. (2018). A Methodology Based on Cyclostationary Analysis for Fault Detection of Hydraulic Axial Piston Pumps. Energies, 11(7), 1-19.
Chen, K., Li, X., Wang, F., Wang, T., and Wu, C. (2012). Bearing fault diagnosis using wavelet analysis. In International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, pages 699–702.
Dai, Y., Xue, Y., and Zhang, J. (2016). A continuous wavelet transform approach for harmonic parameters estimation in the presence of impulsive noise. Journal of Sound and Vibration, 360:300 – 314.
Didier, G. (2004). Modélisation et diagnostic de la machine asynchrone en présence de défaillances. PhD thesis, Université Nancy 1, France.
Do, V. T. and Chong, U.-P. (2011). Signal model-based fault detection and diagnosis for induction motors using features of vibration signal in two- dimension domain. Journal of Mechanical Engineering, 57(9):655–666.
Dron, J. P., Bolaers, F., Rasolofondraibe, I. (2004). Improvement of the sensitivity of the scalar indicators (crest factor, kurtosis) using a de-noising method by spectral subtraction: application to the detection of defects in ball bearings. Journal of Sound and Vibration, 270(1-2), 61-73.
El Badaoui, M. (1999). Contribution to the Vibratory Diagnosis of the Complex gears by the Cepstrum analysis. Theses, Université Jean Monnet - Saint-Etienne.
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.
Feng, Z. and Liang, M. (2014). Fault diagnosis of wind turbine planetary gearbox under nonstationary conditions via adaptive optimal kernel time-frequency analysis. Renewable Energy, 66:468 – 477.
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.
Franklin, G. F., Da Powell, J., & Emami-Naeini, A. (2010) Feedback Control of Dynamic Systems (6th).
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.
Holroyd, T. J. (2005). The application of ae in condition monitoring. In International Conference on Condition Monitoring, volume 47, pages 481–485.
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.
Leite, V. C. M. N., da Silva, J. G. B., Veloso, G. F. C., da Silva, L. E. B., Lambert-Torres, G., Bonaldi, E. L., and d. L. de Oliveira, L. E. (2015). Detection of localized bearing faults in induction machines by spectral kurtosis and envelope analysis of stator current. IEEE Transactions on Industrial Electronics, 62(3):1855–1865.
Liu, B., Ling, S., and Gribonval, R. (2002). Bearing failure detection using matching pursuit. NDT & E International, 35(4):255 – 262.
Li, M.-a., Liu, H.-n., Zhu, W., and Yang, J. (2017). Applying improved multiscale fuzzy entropy for feature extraction of mi-eeg. Applied Sciences, 7:92.
Li, X., Wang, X., Rong, M., Xie, D., Yin, N., Fu, Y., and Gao, Q. (2016a). Comparison of different timefrequency analysis methods for sparse representation of pd-induced uhf signal. In 2016 China International Conference on Electricity Distribution (CICED), pages 1–5.
Li, Y., Xu, M., Wang, R., and Huang, W. (2016b). A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy. Journal of Sound and Vibration, 360:277 – 299.
Ma, J., Wu, J., & Wang, X. (2018). A hybrid fault diagnosis method based on singular value difference spectrum denoising and local mean decomposition for rolling bearing. Journal of Low Frequency Noise, Vibration and Active Control, 1461348418765973.
Mallat, S. G. (1989). A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7):674–693.
Mallat, S. G. and Zhang, Z. (1993). Matching pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing, 41(12):3397–3415.
Mba, D. (2006). Development of acoustic emission technology for condition monitoring and diagnosis of rotating machines: Bearings, pumps, gearboxes, engines, and rotating structures. 38:3–16.
Millioz, F. and Martin, N. (2011). Circularity of the stft and spectral kurtosis for time-frequency segmentation in gaussian environment. IEEE Transactions on Signal Processing, 59(2):515–524.
Nagaraju, C.and Narayana Rao, K. and Mallikarijuna Rao, K. (2009). Application of 3d wavelet transforms for crack detection in rotor systems. Sadhana, 34(3):407–419.
Niu, G., Lau, D., and Pecht, M. (2010). Improving computer manufacturing management through lean six sigma and phm. In Prognostics and System Health Management Conference, pages 1–7.
Niu, G. and Yang, B.-S. (2010). Intelligent condition monitoring and prognostics system based on data-fusion strategy. Expert Systems with Applications, 37(12):8831 – 8840.
Niu, G. (2017). Data-Driven Technology for Engineering Systems Health Management. Springer.
Omar, F. K. and Gaouda, A. (2012). Dynamic waveletbased tool for gearbox diagnosis. Mechanical Systems and Signal Processing, 26:190 – 204.
Oppenheim, A. V. and Schafer, R. W. (2004). From frequency to quefrency: a history of the cepstrum. IEEE Signal Processing Magazine, 21(5):95–106.
Ozturk, H., Sabuncu, M., and Yesilyurt, I. (2008). Early detection of pitting damage in gears using mean frequency of scalogram. Journal of Vibration and Control, 14(4):469–484.
Pang, B., Tang, G., Tian, T., & Zhou, C. (2018). Rolling Bearing Fault Diagnosis Based on an Improved HTT Transform. Sensors, 18(4), 1203.
Park, C., Looney, D., Hulle, M. M. V., and Mandic, D. P. (2011). The complex local mean decomposition. Neurocomputing, 74(6):867 – 875.
Peeters, C., Guillaume, P., & Helsen, J. (2018). Vibrationbased bearing fault detection for operations and maintenance cost reduction in wind energy. Renewable Energy, 116, 74-87.
Prieto, M. D., Cirrincione, G., Espinosa, A. G., Ortega, J. A., and Henao, H. (2013). Bearing fault detection by a novel condition-monitoring scheme based on statisticaltime features and neural networks. IEEE Transactions on Industrial Electronics, 60(8):3398–3407.
Q. Zhu, Y. W. and Shen, G. (2012). Research and comparison of time-frequency techniques for nonstationary signals. Journal of computers, 7(4):954–958.
Rafiee, J. and Tse, P. (2009). Use of autocorrelation of wavelet coefficients for fault diagnosis. Mechanical Systems and Signal Processing, 23(5):1554 – 1572.
Rasovska, I., Chebel-Morello, B., and Zerhouni, N. (2007). Classification des différentes architectures en maintenance. In International congress on electrical engineering, volume 23, pages 1–12.
Roemer, M. J. and Kacprzynski, G. J. (2000). Advanced diagnostics and prognostics for gas turbine engine risk assessment. In IEEE Aerospace Conference, volume 6, pages 345–353 vol.6.
Saidi, L., Fnaiech, F., Capolino, G. A., and Henao, H. (2012). Stator current bi-spectrum patterns for induction machines multiple-faults detection. In IECON 2012 -38th Annual Conference on IEEE Industrial Electronics Society, pages 5132–5137.
Sawalhi, N. (2007). Rolling element bearings: Diagnostic, prognostic and fault simulations. Theses, Faculty Eng. Mech. Manuf. Eng., Univ. New South Wales.
Schmidt, R. (1986). Multiple emitter location and signal parameter estimation. IEEE Transactions on Antennas and Propagation, 34(3):276–280.
Serbes, G., Gulcur, H. O., and Aydin, N. (2016). Directional dual-tree complex wavelet packet transforms for processing quadrature signals. Medical & Biological Engineering & Computing, 54(2):295–313.
Seryasat, O. R., shoorehdeli, M. A., Honarvar, F., and Rahmani, A. (2010). Multi-fault diagnosis of ball bearing based on features extracted from time-domain and multiclass support vector machine (msvm). In IEEE International Conference on Systems, Man and Cybernetics, pages 4300–4303.
Soualhi, A., Medjaher, K., and Zerhouni, N. (2015). Bearing health monitoring based on hilbert huang transform, support vector machine, and regression. IEEE Transactions on Instrumentation and Measurement, 64(1):52–62.
Soualhi, A., Razik, H., Clerc, G., and Doan, D. D. (2014). Prognosis of bearing failures using hidden markov models and the adaptive neuro-fuzzy inference system. IEEE Transactions on Industrial Electronics, 61(6):2864–2874.
Starr, A. G. (1997). A structured approach to the selection of condition based maintenance. In Fifth International Conference on Factory 2000 - The Technology Exploitation Process, pages 131–138.
Swearingen, K., Majkowski, W., Bruggeman, B., Gilbertson, D., Dunsdon, J., and Sykes, B. (2007). An open system architecture for condition based maintenance overview. In IEEE Aerospace Conference, pages 1–8.
Sweldens, W. (1998). The lifting scheme: A construction of second generation wavelets. 29:511–546.
Tandon, N. and Choudhury, A. (1999). A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International, 32(8):469 – 480.
Thomas, M. (2002). Fiabilité, maintenance prédictive et vibration des machines. Université du Québec, Ecole de technologie supérieure.
Thurston, M. (2001a). An open standard for web-based condition-based maintenance systems. In Annual Maintenance and Reliability Conference, pages 401 – 415.
Thurston, M. G. (2001b). An open standard for web-based condition-based maintenance systems. In IEEE Autotestcon Proceedings. IEEE Systems Readiness Technology Conference., pages 401–415.
Tobon-Mejia, D., Medjaher, K., and Zerhouni, N. (2012). Cnc machine tool’s wear diagnostic and prognostic by using dynamic bayesian networks. Mechanical Systems and Signal Processing, 28:167 – 182.
Tsoumas, I., Mitronikas, E., Georgoulas, G., and Safacas, A. (2005). A comparative study of induction motor current signature analysis techniques for mechanical faults detection. In 5th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, pages 1–6.
Tsui, K.-L., Chen, N., Zhou, Q., Hai, Y., and Wang, W. (2015). Prognostics and health management: A review on data driven approaches. 2015:1–17.
Walter, T. J. and Lee, H. (2004). Development of a smart wireless sensor for predicting bearing remaining useful life. In 58th Meeting of the society for machinery failure prevention technology, page 77.
Wang, S., Pentney, W., Popescu, A.-M., Choudhury, T., and Philipose, M. (2007). Common sense based joint training of human activity recognizers. In Proceedings of the 20th International Joint Conference on Artifical Intelligence, pages 2237–2242.
Wang, W. and McFadden, P. (1996). Application of wavelets to gearbox vibration signals for fault detection. Journal of Sound and Vibration, 192(5):927 – 939.
Wang, X., Makis, V., and Yang, M. (2010a). A wavelet approach to fault diagnosis of a gearbox under varying load conditions. Journal of Sound and Vibration, 329(9):1570 – 1585.
Wang, Y., He, Z., and Zi, Y. (2010b). Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform. Mechanical Systems and Signal Processing, 24(1):119 – 137.
Wang, Y., Ma, Q., Zhu, Q., Liu, X., and Zhao, L. (2014). An intelligent approach for engine fault diagnosis based on hilbert-huang transform and support vector machine. Applied Acoustics, 75:1 – 9.
Yam, R. C. M., Tse, P., Li, L., and Tu, P. (2001). Intelligent predictive decision support system for condition-based maintenance. The International Journal of Advanced Manufacturing Technology, 17(5):383–391.
Yan, R., Gao, R. X., and Chen, X. (2014). Wavelets for fault diagnosis of rotary machines: A review with applications. Signal Processing, 96:1 – 15.
Yang, Y. and Nagarajaiah, S. (2014). Blind identification of damage in time-varying systems using independent component analysis with wavelet transform. Mechanical Systems and Signal Processing, 47(1):3 – 20.
Yu, L., Cleary, D., Osborn, M., and Rajiv, V. (2007). Information fusion strategy for aircraft engine health management. In Power for Land, Sea, and Air, volume 1, pages 531–538.
Yuan, J., He, Z., and Zi, Y. (2010). Gear fault detection using customized multiwavelet lifting schemes. Mechanical Systems and Signal Processing, 24(5):1509 – 1528.
Section
Technical Papers