Gear Fault Detection using Machine Learning Techniques- A Simulation-driven Approach

Document Type : Original Article

Authors

Department of Mechanical Engineering, Veermata Jijabai Technological Institute (VJTI), Mumbai, India

Abstract

Machine Learning (ML) based condition monitoring and fault detection of industrial equipment is the current scenario for maintenance in the era of Industry-4.0. The application of ML techniques for automatic fault detection minimizes the unexpected breakdown of the system. However, these techniques heavily rely on the historical data of equipment for its training which limits its widespread application in industry. As the historical data is not available for each industrial machine and generating the data experimentally for each fault condition is not viable. Therefore, this challenge is addressed for gear application with tooth defect. In this paper, ML algorithms are trained using simulated vibration data of the gearbox and tested with the experimental data. Simulated data is generated for the gearbox with different operating and fault conditions. A gearbox dynamic model is utilized to generate simulated vibration data for normal and faulty gear condition. A pink noise is added to simulated data to improve the exactness to the actual field data.  Further, these simulated-data are processed using Empirical Mode Decomposition and Discrete Wavelet Transform, and features are extracted. These features are then fed to the training of different well-established ML techniques such as Support Vector Machine, Random Forest and Multi-Layer Perceptron. To validate this approach, trained ML algorithms are tested using experimental data. The results show more than 87% accuracy with all three algorithms. The performance of the trained model is evaluated using precision, recall and ROC curve. These metric show the affirmative results for the applicability of this approach in gear fault detection.

Keywords


Salameh, J. P., Cauet, S., Etien, E., Sakout, A., & Rambault, L., “Gearbox condition monitoring in wind turbines: A review”, Mechanical Systems and Signal Processing, Vol. 111, (2018), 251-264. DOI: 10.1016/j.ymssp.2018.03.052
Wang, K., “Intelligent predictive maintenance (IPdM) system–Industry 4.0 scenario”, WIT Transactions on Engineering Sciences, Vol. 113, (2016), 259-268. DOI: 10.2495/IWAMA150301
Karimzadeh, A., & Shoghli, O., “Predictive Analytics for Roadway Maintenance: A Review of Current Models, Challenges, and Opportunities”, Civil Engineering Journal, Vol. 6, No. 3, (2020), 602-625. DOI: 10.28991/cej-2020-03091495
Damuluri, S., Islam, K., Ahmadi, P., & Qureshi, N. S., “Analyzing Navigational Data and Predicting Student Grades Using Support Vector Machine”, Emerging Science Journal, Vol. 4, No. 4, (2020), 243-252. DOI: 10.28991/esj-2020-01227
Liang X, Zuo MJ, Feng Z., “Dynamic modeling of gearbox faults: A review”, Mechanical Systems and Signal Processing, Vol. 98, (2018), 852-876. DOI: 10.1016/j.ymssp.2017.05.024
Lei Y, Yang B, Jiang X, Jia F, Li N, Nandi AK., “Applications of machine learning to machine fault diagnosis: A review and roadmap”, Mechanical Systems and Signal Processing, Vol. 138, (2020), 106587. DOI: 10.1016/j.ymssp.2019.106587
Samanta, B., “Gear fault detection using artificial neural networks and support vector machines with genetic algorithms”, Mechanical systems and signal processing, Vol. 18, No. 3, (2004), 625-644. DOI: 10.1016/S0888-3270(03)00020-7
Samanta B, Al-Balushi KR, Al-Araimi SA., “Artificial neural networks and genetic algorithm for bearing fault detection”, Soft Computing, Vol. 10, No. 3, (2006), 264-271. DOI: 10.1007/s00500-005-0481-0
Tyagi, S., Panigrahi, S. K., “A DWT and SVM based method for rolling element bearing fault diagnosis and its comparison with Artificial Neural Networks”, Journal of Applied and Computational Mechanics, Vol. 3, No. 1, (2017), 80-91. DOI: 10.22055/JACM.2017.21576.1108
Sanz J, Perera R, Huerta C., “Gear dynamics monitoring using discrete wavelet transformation and multi-layer perceptron neural networks”, Applied Soft Computing, Vol. 12, No. 9, (2012), 2867-2878. DOI: 10.1016/j.asoc.2012.04.003
Shen Z, Chen X, Zhang X, He Z., “A novel intelligent gear fault diagnosis model based on EMD and multi-class TSVM”, Measurement, Vol. 45, No. 1, (2012), 30-40. DOI: 10.1016/j.measurement.2011.10.008
Shao R, Hu W, Huan X, Chen L., “Multi-damage feature extraction and diagnosis of a gear system based on higher order cumulant and empirical mode decomposition”, Journal of Vibration and Control, Vol. 21, No. 4, (2015), 736-754. DOI: 10.1177/1077546313482342
Li, X., Han, L., Xu, H., Yang, Y. and Xiao, H., “Rolling Bearing Fault Analysis by Interpolating Windowed DFT Algorithm”, International Journal of Engineering, Transactions A: Basics, Vol. 32, No. 1, (2019), 121-126. DOI: 10.5829/ije.2019.32.01a.16
Dhamande LS, Chaudhari MB., “Compound gear-bearing fault feature extraction using statistical features based on time-frequency method”, Measurement, Vol. 125, (2018), 63-77. DOI: 10.1016/j.measurement.2018.04.059
Attaran, B., Ghanbarzadeh, A. and Moradi, S., “A Novel Intelligent Fault Diagnosis Approach for Critical Rotating Machinery in the Time-frequency Domain”, International Journal of Engineering, Transactions A: Basics, Vol. 33, No. 4, (2020), 668-675. DOI: 10.5829/IJE.2020.33.04A.18
Bajric R, Zuber N, Skrimpas GA, Mijatovic N.,  “Feature extraction using discrete wavelet transform for gear fault diagnosis of wind turbine gearbox”, Shock and Vibration, (2016), DOI: 10.1155/2016/6748469
Han T, Jiang D., “Rolling bearing fault diagnostic method based on VMD-AR model and random forest classifier”, Shock and Vibration, (2016), DOI: 10.1155/2016/5132046
Cerrada M, Zurita G, Cabrera D, Sánchez RV, Artés M, Li C., “Fault diagnosis in spur gears based on genetic algorithm and random forest”, Mechanical Systems and Signal Processing, Vol. 70-71, (2016), 87-103. DOI: 10.1016/j.ymssp.2015.08.030
Patil S, Phalle V., “Fault Detection of Anti-friction Bearing using Ensemble Machine Learning Methods”, International Journal of Engineering, Transactions B: Applications, Vol. 31, No. 11, (2018), 1972-1981. DOI: 10.5829/ije.2018.31.11b.22
Bartelmus W., “Mathematical modelling and computer simulations as an aid to gearbox diagnostics”, Mechanical Systems and Signal Processing, Vol. 15, No. 5, (2001), 855-871. DOI: 10.1006/mssp.2001.1411
Howard I, Jia S, Wang J., “The dynamic modelling of a spur gear in mesh including friction and a crack”, Mechanical Systems and Signal Processing, Vol. 15, No. 5, (2001), 831-853. DOI: 10.1006/mssp.2001.1414
Abouel-seoud SA, Dyab ES, Elmorsy MS., “Influence of tooth pitting and cracking on gear meshing stiffness and dynamic response of wind turbine gearbox”, International Journal of. Science and Advanced. Technology, Vol. 2, No. 3, (2012), 151-165. DOI:
Mohammed, O. D., Rantatalo, M., &Aidanpää, J. O., “Dynamic modelling of a one-stage spur gear system and vibration-based tooth crack detection analysis”, Mechanical Systems and Signal Processing, Vol. 54-55, (2015), 293-305. DOI: 10.1016/j.ymssp.2014.09.001
Parey A, El Badaoui M, Guillet F, Tandon N., “Dynamic modelling of spur gear pair and application of empirical mode decomposition-based statistical analysis for early detection of localized tooth defect”, Journal of Sound and vibration, Vol. 294, No. 3, (2006), 547-561. DOI: 10.1016/j.jsv.2005.11.021
He Q, Li P, Kong F., “Rolling bearing localized defect evaluation by multiscale signature via empirical mode decomposition”, Journal of Vibration and Acoustics, Vol. 134. No. 6, (2012). DOI: 10.1115/1.4006754
Handikherkar V.C., Phalle V.M., “Vibration Analysis Based Spalling Defect Severity Assessment of Spur Gearbox Using a Dynamic Model”, Developments and Novel Approaches in Nonlinear Solid Body Mechanics, Vol.130, (2020), 363-375. DOI: 10.1007/978-3-030-50460-1_20
Wu S, Zuo MJ, Parey A., “Simulation of spur gear dynamics and estimation of fault growth”, Journal of Sound and Vibration, Vol. 317, No. (3-5), (2008), 608-624. DOI: 10.1016/j.jsv.2008.03.038
Chaari F, Baccar W, Abbes MS, Haddar M., “Effect of spalling or tooth breakage on gearmesh stiffness and dynamic response of a one-stage spur gear transmission”, European Journal of Mechanics-A/Solids, Vol. 27, No. 4, (2008), 691-705. DOI: 10.1016/j.euromechsol.2007.11.005
Bak, P., Tang, C., &Wiesenfeld, K., “Self-organized criticality: An explanation of the 1/f noise”, Physical Review Letters, Vol. 59, No. 4, (1987), 381. DOI: 10.1103/PhysRevLett.59.381
Chen, B., Yan, Z., & Chen, W., “Defect detection for wheel-bearings with time-spectral kurtosis and entropy”, Entropy, Vol. 16, No. 1, (2014), 607-626. DOI: 10.3390/e16010607