The Detection of Rolling-Element Bearing Faults in Non-stationary Quasi-Parallel Machinery Using Residual Analysis Augmented by Neural Networks



Published Sep 20, 2021
Dustin Helm Markus Timusk


This work proposes a methodology for the detection of rolling-element bearing faults in quasi-parallel machinery. In the context of this work, parallel machinery is considered to be any group of identical components of a mechanical system that are linked to operate on the same duty cycle.  Quasi-parallel machinery can further be defined as two components not identical mechanically, but their operating conditions are correlated and they operate in the same environmental conditions. Furthermore, a new fault detection architecture is proposed wherein a feed-forward neural network (FFNN) is utilized to identify the relationship between signals. The proposed technique is based on the analysis of a calculated residual between feature vectors from two separate components. This technique is designed to reduce the effects of changes in the machines operating state on the condition monitoring system. When a fault detection system is monitoring multiple components in a larger system that are mechanically linked, signals and information that can be gleaned from the system can be used to reduce influences from factors that are not related to condition. The FFNN is used to identify the relationship between the feature vectors from two quasi-parallel components and eliminate the difference when no fault is present. The proposed method is tested on vibration data from two gearboxes that are connected in series. The gearboxes contain bearings operating at different speeds and gear mesh frequencies. In these conditions, a variety of rolling-element bearing faults are detected. The results indicate that improvement in fault detection accuracy can be achieved by using the additional information available from the quasi-parallel machine. The proposed method is directly compared to a typical AANN novelty detection scheme.

Abstract 51 | PDF Downloads 32



Condition Monitoring, Hardware Redundancy, Non-Stationary Operating Conditions, Auto-Associative Neural Network

Abboud, D., Antoni, J., Sieg-Zieba, S., & Eltabach, M. (2017). Envelope analysis of rotating machine vibrations in variable speed conditions: A comprehensive treatment. Mechanical Systems and Signal Processing, 84, 200–226.
Abboud, D., Baudin, S., Antoni, J., Remond, D., Eltabach, M., & Sauvage, O. (2016). The spectral analysis of cyclo-non-stationary signals. Mechanical Systems and Signal Processing, 75, 280–300.
Barakat, M., Druaux, F., Lefebvre, D., Khalil, M., & Mustapha, O. (2011). Self adaptive growing neural network classifier for faults detection and diagnosis. Neurocomputing, 74(18), 3865–3876.
Bin, G. F., Gao, J. J., Li, X. J., & Dhillon, B. S. (2012). Early fault diagnosis of rotating machinery based on wavelet packets - Empirical mode decomposition feature extraction and neural network. Mechanical Systems and Signal Processing, 27(1), 696–711.
Chen, Z., & Mechefske, C. K. (2001). Model order selection: A practical approach. Mechanical Systems and Signal Processing, 15(2), 265–273.
Cong, F., Chen, J., & Dong, G. (2012). Spectral kurtosis based on AR model for fault diagnosis and condition monitoring of rolling bearing. Journal of Mechanical Science and Technology, 26(2), 301–306.
Figueiredo, E., Figueiras, J., Park, G., Farrar, C. R., & Worden, K. (2011). Influence of the autoregressive model order on damage detection. Computer-Aided Civil and Infrastructure Engineering, 26(3), 225–238.
Gertler, J. (1997). Fault detection and isolation using parity relations. Control Engineering Practice, 5(5), 653–661.
Gianluca, N., Fromaigeat, L., & Etienne, L. (2016). Machine Learning Strategy for Fault Classification Using Only Nominal Data. In European Conference Of The Prognostics And Health Management Society.
Gor, M. M., Pathak, P. M., Samantaray, A. K., Yang, J.-M., & Kwak, S. W. (2018). Fault accommodation in compliant quadruped robot through a moving appendage mechanism. Mechanism and Machine Theory, 121, 228–244.
Hagan, M. T., & Menhaj, M. B. (1994). Training feed-forward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5(6), 989–993.
Haidong, S., Hongkai, J., Xingqiu, L., & Shuaipeng, W. (2018). Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine. Knowledge-Based Systems, 140, 1–14.
Hao, Y., & Wilamowski, B. M. (2011). Levenberg-marquardt training. In Industrial electronics handbook (5th ed., pp. 12-1-12–15).
Helm, D. M., Rose, A. M., & Timusk, M. (2016). Condition monitoring of rolling-element bearings in parallel operating belt drive systems. International Journal of COMADEM, 19(3), 61–64.
Helm, D., & Timusk, M. (2017). Using residual analysis for detection of faults in unsteadily operating rolling element bearings. In WCCM 2017 - 1st World Congress on Condition Monitoring 2017.
Helm, D., & Timusk, M. (2019). Fault detection for parallel operating machines. Journal of Quality in Maintenance Engineering, 26(2), 335–348.
Isermann, R., & Ballé, P. (1997). Trends in the application of model-based fault detection and diagnosis of technical processes. Control Engineering Practice, 5(5), 709–719.
Japkowicz, N., Myers, C., & Gluck, M. (1995). A Novelty Detection Approach to Classification. In 14th International Joint Conference on Artificial Intelligence (pp. 518–523).
Jia, F., Lei, Y., Lin, J., Zhou, X., & Lu, N. (2016). Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mechanical Systems and Signal Processing, 72–73, 303–315.
Jiang, H., Wang, F., Shao, H., & Zhang, H. (2017). Rolling bearing fault identification using multilayer deep learning convolutional neural network. Journal of Vibroengineering, 19(1), 138–149.
Liu, R., Yang, B., Zio, E., & Chen, X. (2018). Artificial intelligence for fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 108, 33–47.
Kramer, M.A. (1992). Autoassociative neural networks. Computers and Chemical Engineering, 16(4), 313–328.
Kramer, Mark A. (1991). Nonlinear principal component analysis using autoassociative neural networks. AIChE Journal, 37(2), 233–243. Retrieved from
Lei, Y., Lin, J., He, Z., & Zuo, M. J. (2013). A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, 35(1–2), 108–126.
Liu, R., Yang, B., Zio, E., & Chen, X. (2018). Artificial intelligence for fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 108, 33–47.
McBain, J., Lakanen, G., & Timusk, M. (2013). Vibration- and acoustic-emissions based novelty detection of fretted bearings. Journal of Quality in Maintenance Engineering, 19(2), 181–198.
Principi, E., Rossetti, D., Squartini, S., & Piazza, F. (2019). Unsupervised electric motor fault detection by using deep autoencoders. IEEE/CAA Journal of Automatica Sinica, 6(2), 441–451.
Randall, R. B., & Antoni, J. (2011). Rolling element bearing diagnostics-A tutorial. Mechanical Systems and Signal Processing, 25(2), 485–520.
Rose, A. M., Helm, D. M., & Timusk, M. (2016). Fault detection of parallel hydraulic pumps in non-stationary operation. International Journal of COMADEM, 19(3), 55–59.
Samanta, B. (2004). Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mechanical Systems and Signal Processing, 18(3), 625–644.
Samanta, B., & Al-Balushi, K. R. (2003). Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mechanical Systems and Signal Processing, 17(2), 317–328.
Sanz, J., Perera, R., & Huerta, C. (2007). Fault diagnosis of rotating machinery based on auto-associative neural networks and wavelet transforms. Journal of Sound and Vibration, 302(4–5), 981–999.
Shao, H., Jiang, H., Zhao, H., & Wang, F. (2017). A novel deep autoencoder feature learning method for rotating machinery fault diagnosis. Mechanical Systems and Signal Processing, 95, 187–204.
Staroswiecki, M., & Comtet-Varga, G. (2001). Analytical redundancy relations for fault detection and isolation in algebraic dynamic systems. Automatica, 37(5), 687–699.
Strczkiewicz, M., & Barszcz, T. (2016). Application of artificial neural network for damage detection in planetary gearbox of wind turbine. Shock and Vibration, 2016.
Timusk, M., Lipsett, M., & Mechefske, C. K. (2008). Fault detection using transient machine signals. Mechanical Systems and Signal Processing, 22(7), 1724–1749.
Wang, G., & Cui, Y. (2013). On line tool wear monitoring based on auto associative neural network. Journal of Intelligent Manufacturing, 24(6), 1085–1094.
Wang, X., & Makis, V. (2009). Autoregressive model-based gear shaft fault diagnosis using the Kolmogorov-Smirnov test. Journal of Sound and Vibration, 327(3–5), 413–423.
Willersrud, A., Blanke, M., & Imsland, L. (2015). Incident detection and isolation in drilling using analytical redundancy relations. Control Engineering Practice, 41, 1–12.
Xie, Y., & Zhang, T. (2017). Fault Diagnosis for Rotating Machinery Based on Convolutional Neural Network and Empirical Mode Decomposition. Shock and Vibration, 2017, 1–12.
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115, 213–237.
Technical Papers