On the Usefulness of Pre-processing Methods in Rotating ‎Machines Faults Classification using Artificial Neural Network

Document Type : Research Paper


1 Data Science Department, Princess Sumaya University for Technology, Amman, Jordan

2 Electrical Engineering Department, University of Sharjah, Sharjah, United Arab Emirates

3 Sustainable Engineering Asset Management (SEAM) Research Group, University of Sharjah, Sharjah, United Arab Emirates

4 Industrial Engineering and Engineering Management Department, University of Sharjah, Sharjah, United Arab Emirates


This work presents a multi-fault classification system using artificial neural network (ANN) to distinguish between different faults in rotating machines automatically. Rotation frequency and statistical features, including mean, entropy, and kurtosis were considered in the proposed model. The effectiveness of this model lies in using Synthetic Minority Over-sampling Technique (SMOTE) to overcome the problem of imbalance data classes. Furthermore, the Relief feature selection method was used to find the most influencing features and thus improve the performance of the model. Machinery Fault Database (MAFAULDA) was deployed to evaluate the performance of the prediction models, achieving an accuracy of 97.1% which surpasses other literature that used the same database. Results indicate that handling imbalance classes hold a key role in increasing the overall accuracy and generalizability of multi-layer perceptron (MLP) classifier. Furthermore, results showed that considering only statistical features and rotational speed are good enough to get a model with high classification accuracy.


Main Subjects

[1] Bingamil, A., Alsyouf, I., Cheaitou, A., Condition monitoring technologies, parameters and data processing techniques for fault detection of internal combustion engines: A literature review, ICETA International Conference on Electrical and Computing Technologies and Applications, Ras Al Khaimah, UAE, 2017.
[2] Reda, K., Yan, Y., Online continuous detection of an unbalanced metallic shaft using electrostatic sensors, I2MTC IEEE International Instrumentation and Measurement Technology Conference: Discovering New Horizons in Instrumentation and Measurement, Houston, TX,2018.
[3] Kumar, C., Krishnan, G., Sarangi, S., Experimental investigation on misalignment fault detection in induction motors using current and vibration signature analysis, ABLAZE 1st International Conference on Futuristic Trends in Computational Analysis and Knowledge Management, Noida, India,2015.
[4] Liu, Z., Cao, H., Chen, X., He, Z., Shen, Z., Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings, Neurocomputing, 99, 2013, 399–410.
[5] Giraldo, E., Verucchi, C., Acosta, G., Ferrari, M., Detection of misalignment in elastic couplings through fuzzy logic, RPIC 17th Workshop on Information Processing and Control, Mar del Plata , Argintine, 2017.
[6] 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, 3(1), 2017, 80–91.
[7] Attaran, B., Ghanbarzadeh, A., Bearing Fault Detection Based on Maximum Likelihood Estimation and Optimized ANN Using the Bees Algorithm, Journal of Applied and Computational Mechanics, 1(1), 2014, 35–43.
[8] Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., Arshad, H., State-of-the-art in artificial neural network applications : A survey, Heliyon, 4, 2018, e00938.
[9] Ambrogio, S. et al., Equivalent-accuracy accelerated neural-network training using analogue memory, Nature, 558, 2018, 60–67.
[10] Yao, P. et al., Fully hardware-implemented memristor convolutional neural network, Nature, 577, 2020, 641–646.
[11] Randall, R. B., Vibration-based Condition Monitoring: INDUSTRIAL, AEROSPACE AND AUTOMOTIVE APPLICATIONS. John Wiley and Sons, Australia, 2011.
[12] Sugumaran, V., Ramachandran, K. I., Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing, Mechanical Systems and Signal Processing, 21(5), 2007, 2237–2247.
[13] Marins, M., Ribeiro, F., Netto, S., da Silva, E. , Improved similarity-based modeling for the classification of rotating-machine failures, Journal of the Franklin Institute, 355(4), 2018, 1913–1930.
[14] Bordoloi, D. J., Tiwari, R., Support vector machine based optimization of multi-fault classification of gears with evolutionary algorithms from time-frequency vibration data, Measurement: Journal of the International Measurement Confederation, 55, 2014, 1–14.
[15] Tang, X., Zhuang, L., Cai, J., Li, C., Multi-fault classification based on support vector machine trained by chaos particle swarm optimization, Knowledge-Based Systems, 23(5), 2010, 486–490.
[16] Fatima, S., Guduri, B., Mohanty, A. R., Naikan, V. N. A., Transducer invariant multi-class fault classification in a rotor-bearing system using support vector machines, Measurement: Journal of the International Measurement Confederation, 58, 2014, 363–374.
[17] Wu, S. De, Wu, P. H., Wu, C. W., Ding, J. J., Wang, C. C., Bearing fault diagnosis based on multiscale permutation entropy and support vector machine, Entropy, 14(8), 2012, 1343–1356.
[18] Shen, C., Wang, D., Kong, F., Tse, P. W., Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier, Measurement: Journal of the International Measurement Confederation, 46(4), 2013, 1551–1564.
[19] Konar, P., Chattopadhyay, P., Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs), Applied Soft Computing, 11(6), 2011, 4203–4211.
[20] Chandel, A. K., Patel, R. K., Bearing fault classification based on wavelet transform and artificial neural network, IETE Journal of Research, 59(3), 2013, 219–225.
[21] Unal, M., Onat, M., Demetgul, M., Kucuk, H., Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network, Measurement: Journal of the International Measurement Confederation, 58, 2014, 187–196.
[22] Hajnayeb, A., Ghasemloonia, A., Khadem, S. E., Moradi, M. H., Application and comparison of an ANN-based feature selection method and the genetic algorithm in gearbox fault diagnosis, Expert Systems with Applications, 38(8), 2011, 10205–10209.
[23] Chen, Z., Li, C., Sanchez, R.-V., Gearbox fault identification and classification with convolutional neural network, Shock and Vibration, 2015, 1–10.
[24] Wang, C. C., Kang, Y., Shen, P. C., Chang, Y. P., Chung, Y. L., Applications of fault diagnosis in rotating machinery by using time series analysis with neural network, Expert Systems with Applications, 37(2), 2010, 1696–1702.
[25] Jia, F., Lei, Y., Lin, J., Zhou, X., Lu, N., 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, 2016, 303–315.
[26] Pestana-Viana, D., Zambrano-López, R., De Lima, A. A., De Prego, M. T., Netto, S. L., Da Silva, E. A. B., The influence of feature vector on the classification of mechanical faults using neural networks, LASCAS 7th IEEE Latin American Symposium on Circuits and Systems, Florianopolis, Brazil, 2016.
[27] Zhang, W., Li, X., Jia, X. D., Ma, H., Luo, Z., Li, X., Machinery fault diagnosis with imbalanced data using deep generative adversarial networks, Measurement: Journal of the International Measurement Confederation, 152, 2020, 107377.
[28] Kira, K., Rendell, L. A., The Feature selection Problem: Traditional Methods and A New Algorithm, AAAI-92 Proceedings, 1992.
[29] Spolaôr, N., Cherman, E. A., Monard, M. C., Lee, H. D., A comparison of multi-label feature selection methods using the problem transformation approach, Electronic Notes in Theoretical Computer Science, 292, 2013, 135–151.
[30] MAFAULDA - Machinery Fault Database. [Online]. Available: http://www02.smt.ufrj.br/~offshore/mfs/page_01.html.
[31] SpectraQuest, Inc. [Online]. Available: http://www.http//spectraquest.com/.
[32] De Lima, A. A. et al., On fault classification in rotating machines using fourier domain features and neural networks, LASCAS IEEE 4th Latin American Symposium on Circuits and Systems, Cusco, Peru, 2013.
[33] Chawla, N. V., Bowyer, K. W., Hall, L. O., Kegelmeyer, W. P., SMOTE: Synthetic Minority Over-sampling Technique, Journal of Artificial Intelligence Research, 16, 2002, 321–357.
[34] Skryjomski, P., Krawczyk, B., Influence of minority class instance types on SMOTE imbalanced data oversampling, Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications, Skopje, Macedonia, 2017.