Simulation-driven Deep Classification of Bearing Faults from Raw Vibration Data



Published Dec 2, 2019
Martin Hemmer Andreas Klausen Huynh van Khang Kjell G. Robbersmyr Tor I. Waag


The industry is moving towards maintenance strategies that consider component health, which require extensive collection and analysis of data. Condition monitoring methods that require manual feature extraction and analysis, become infeasible on an industrial scale. Machine learning algorithms can be used to automatically detect and classify faults, however, obtaining sufficient data for training is required for deep learning and other data-driven classification approaches. Data from healthy machine operation is generally available in abundance, while data from representative fault- and operating conditions is limited. This limits both development and deployment of deep learning-based CM systems on an industrial scale. This paper addresses both the challenges of automated analysis and lack of training data. A deep learning classifier architecture utilizing 1-dimensional dilated convolutions is proposed. Dilation of the convolution kernel allows for analysis of raw vibration signals while simultaneously maintaining the receptive field of the classifier enough to capture temporal patterns. The proposed method performs classification in time domain on signal segments of 1 second or shorter. With knowledge of the bearing specification, artificial vibration signals with similar characteristics as an actual bearing fault can be created. In this work, generated fault signals are combined with healthy operational data to obtain
training data for a deep classifier. Parameters of the vibration model is chosen as distributions rather than fixed values. By using a range parameters in the vibration model, the classifier learns to recognize temporal features from the training data that generalize to unseen data. The effectiveness of the proposed method is demonstrated by training classifiers on generated data and testing on real signals from faulty bearings
at both low and high speed. One dataset containing seeded faults and three run-to-failure tests are used for the demonstration.

Abstract 279 | PDF Downloads 321



Convolutional Neural Network, bearing fault diagnosis, Deep Learning for Time-series

Antoni, J. (2006). The Spectral Kurtosis: A Useful Tool for Characterising Non-Stationary Signals. Mech. Syst. Signal Process., 20(2), 282–307. doi: 10.1016/j.ymssp.2004.09.001
Antoni, J. (2007a). Cyclic Spectral Analysis of Rolling-Element Bearing Signals: Facts and Fictions. J. Sound Vib., 304(3-5), 497–529. doi: 10.1016/j.jsv.2007.02.029
Antoni, J. (2007b). Fast Computation of the Kurtogram for the Detection of Transient Faults. Mech. Syst. Signal Process., 21(1), 108–124. doi: 10.1016/j.ymssp.2005.12.002
Antoni, J. (2009). Cyclostationarity by Examples (Vol. 23) (No. 4). doi: 10.1016/j.ymssp.2008.10.010
Antoni, J., & Randall, R. B. (2002). Differential Diagnosis of Gear and Bearing Faults. J. Vib. Acoust., 124(2), 165. doi: 10.1115/1.1456906
Bechhoefer, E., & Kingsley, M. (2009). A Review of Time Synchronous Average Algorithms. In Annu. conf. progn. heal. manag. soc. (pp. 24–33).
Bechhoefer, E., Schlanbusch, R., & Waag, T. I. (2016). Fault Detection on Large Slow Bearings. In Phme 2016 (Vol. 7, pp. 1–8).
Borghesani, P., Pennacchi, P., Randall, R. B., Sawalhi, N., & Ricci, R. (2013). Application of Cepstrum Pre-Whitening for the Diagnosis of Bearing Faults Under Variable Speed Conditions. Mech. Syst. Signal Process., 36(2), 370–384. doi: 10.1016/j.ymssp.2012.11.001
Case Western Reserve University Bearing Data Website. (n.d.).
Chollet, F., & Others. (2015). Keras. nurlfhttps://keras.iog. Diez-Olivan, A., Del Ser, J., Galar, D., & Sierra, B. (2019). Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0. Inf. Fusion, 50, 92–111. doi: 10.1016/J.INFFUS.2018.10.005
Fyfe, K., & Munck, E. (1997). Analysis of Computed Order Tracking. Mech. Syst. Signal Process., 11(2), 187–205. doi: 10.1006/MSSP.1996.0056
Gan, M., Wang, C., & an Zhu. (2015). Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings. Mech. Syst. Signal Process., 72-73, 92–104. doi: 10.1016/j.ymssp.2015.11.014
Guo, X., Chen, L., & Shen, C. (2016). Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis. Measurement, 93, 490–502. doi: 10.1016/J.MEASUREMENT.2016.07.054
He, M., & He, D. (2017). Deep Learning Based Approach for Bearing Fault Diagnosis. IEEE Trans. Ind. Appl., 53(3), 3057–3065. doi:10.1109/TIA.2017.2661250
Hecke, B. V., Yoon, J., & He, D. (2016). Low speed bearing fault diagnosis using acoustic emission sensors. Appl. Acoust., 105, 35–44. doi: 10.1016/j.apacoust.2015.10.028
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A., Jaitly, N., & Kingsbury, B. (2012). Deep Neural Networks for Acoustic Modeling in Speech Recognition. IEEE Signal Process. Mag..
Ho, D., & Randall, R. B. (2000). Optimisation of Bearing Diagnostic Techniques Using Simulated and Actual Bearing Fault Signals. Mech. Syst. Signal Process., 14(5), 763–788. doi: 10.1006/mssp.2000.1304
Ioffe, S., & Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (Tech. Rep.).
Jia, F., Lei, Y., Guo, L., Lin, J., & Xing, S. (2018). A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines. Neurocomputing, 272, 619–628. doi: 10.1016/J.NEUCOM.2017.07.032
Jiang, G., He, H., Yan, J., & Xie, P. (2019). Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox. IEEE Trans. Ind. Electron., 66(4), 3196–3207. doi: 10.1109/TIE.2018.2844805
Khan, M. A., Kim, Y.-H., & Choo, J. (2018). Intelligent Fault Detection via Dilated Convolutional Neural Networks. In 2018 ieee int. conf. big data smart comput. (pp. 729–731). IEEE. doi: 10.1109/BigComp.2018.00137
Klausen, A., Folgerø, R. W., Robbersmyr, K. G., & Karimi, H. R. (2017). Accelerated Bearing Life-time Test Rig Development for Low Speed Data Acquisition. Identif. Control, 38(3), 143–156. doi: 10.4173/mic.2017.3.4
Klausen, A., Robbersmyr, K. G., & Karimi, H. R. (2017). Autonomous Bearing Fault Diagnosis Method based on Envelope Spectrum. IFAC-PapersOnLine, 50(1), 13378–13383. doi: 10.1016/J.IFACOL.2017.08.2262
Li, G., Deng, C., Wu, J., Xu, X., Shao, X., Wang, Y., et al. (2019). Sensor Data-Driven Bearing Fault Diagnosis Based on Deep Convolutional Neural Networks and S-Transform. Sensors, 19(12), 2750. doi: 10.3390/ s19122750
Li, X., Liu, Z., Qu, Y., & He, D. (2018). Unsupervised Gear Fault Diagnosis Using Raw Vibration Signal Based on Deep Learning. In 2018 progn. syst. heal.
manag. conf. (pp. 1025–1030). IEEE. doi: 10.1109/ PHM-Chongqing.2018.00182
Li, X., Yang, Y., Pan, H., Cheng, J., & Cheng, J. (2019). A novel deep stacking least squares support vector machine for rolling bearing fault diagnosis. Comput. Ind., 110, 36–47. doi: 10.1016/J.COMPIND.2019.05.005
Lin, M., Chen, Q., & Yan, S. (2014). Network In Network (Tech. Rep.).
Marple, L. (1999). Computing the Discrete-time ”Analytic” Signal via FFT. IEEE Trans. Signal Process., 47(9), 2600–2603. doi: 10.1109/78.782222
McFadden, P., & Smith, J. (1984). Model for the Vibration Produced by a Single Point Defect in a Rolling Element Bearing. J. Sound Vib., 96(1), 69–82. doi: 10.1016/0022-460X(84)90595-9
Nair, V., & Hinton, G. (2010). Rectified Linear Units Improve Restricted Boltzmann Machines. In Proc. 27th int. conf. mach. learn.
Qiu, H., Lee, J., Lin, J., & Yu, G. (2006). Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. J. Sound Vib., 289, 1066–1090. doi: 10.1016/j.jsv.2005.03.007
Randall, R. B., & Antoni, J. (2011). Rolling Element Bearing Diagnostics - A Tutorial. Mech. Syst. Signal Process., 25(2), 485–520. doi: 10.1016/j.ymssp.2010.07.017
Randall, R. B., & Sawalhi, N. (2011). Use of the Cepstrum to Remove Selected Discrete Frequency Components from a Time Signal. In Proc. int. conf. noise vib. eng. (pp. 451–461). Springer, New York, NY. doi: 10.1007/ 978-1-4419-9428-8 38
Randall, R. B., Sawalhi, N., & Coats, M. (2011). A Comparison of Methods for Separation of Deterministic and Random Signals. Int. J. Cond. Monit., 1(1), 11–19.
Sak, H., Senior, A., & Beaufays, F. (2014). Long shortterm memory recurrent neural network architectures for large scale acoustic modeling. In Proc. annu. conf. int. speech commun. assoc. interspeech.
Sawalhi, N., & Randall, R. B. (2008). Simulating Gear and Bearing Interactions in the Presence of Faults Part I. the Combined Gear Bearing Dynamic Model and the Simulation of Localised Bearing Faults. Mech. Syst. Signal Process., 22(8), 1924–1951. doi: 10.1016/ j.ymssp.2007.12.001
Shao, H., Jiang, H., Zhang, H., Duan, W., Liang, T., & Wu, S. (2018). Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing. Mech. Syst. Signal Process., 100, 743–765. doi: 10.1016/J.YMSSP.2017.08.002
Smith, W. A., & Randall, R. B. (2015). Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study. Mech. Syst. Signal Process., 64-65, 100–131. doi: 10.1016/j.ymssp.2015.04.021
Sobie, C., Freitas, C., & Nicolai, M. (2018). Simulation-Driven Machine Learning: Bearing Fault Classification. Mech. Syst. Signal Process.. doi: 10.1016/
Van Den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., et al. (2016). WaveNet: A Generative Model for Raw Audio (Tech. Rep.).
Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., et al. (2014). Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation (Tech. Rep.).
Zhang, S., Zhang, S., Wang, B., & Habetler, T. G. (n.d.). Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics-A Comprehensive Review (Tech. Rep.).
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019). Deep Learning and Its Applications to Machine Health Monitoring. Mech. Syst. Signal Process., 115, 213–237. doi: 10.1016/J.YMSSP.2018.05.050
Technical Papers