For the past few years, prognostics and health management (PHM) has taken a new dimension due to the advanced developments in algorithms and computing methodologies. Rotating machinery is one of the most extensively used equipment in the industries. Monitoring the working conditions of the rotating machinery becomes crucial to avoid system failure. The early detection of such a failure can help the industries in many ways. In this study, an effective methodology is demonstrated to diagnose the working condition of a three-phase induction motor using only the motor current signature and convolutional neural network (CNN). The electrical current signals are collected for five different types of fault conditions and one healthy condition of the induction motors. The proposed methodology mainly consists of two parts. The first part of the methodology demonstrates the preprocessing techniques, in which the time-series data signals are converted into two-dimensional (2-D) images. Two pattern recognition techniques based on recurrence plots (RPs) and empirical wavelet transform (EWT) are studied, to transform the raw one-dimensional (1-D) time-series current signals into 2-D RP and EWT images. The second part of the methodology explains how the proposed CNN automatically extracts the robust features from the images to diagnose the faults in the induction motors. The proposed methodology is studied using the dataset collected from different 3-phase induction motors working with different failure modes on full load conditions. The experimental results of the proposed methodology achieve competitive performance over traditional and other machine/deep learning methodologies. The CNN can be extensively applied to study the fault diagnosis of induction motors using the RPs and EWT images as a new feature extraction technique for time-series input signals such as current, vibration, temperature, and acoustics.

For the past few years, prognostics and health management (PHM) has taken a new dimension due to the advanced developments in algorithms and computing methodologies. Rotating machinery is one of the most extensively used equipment in the industries. Monitoring the working conditions of the rotating machinery becomes crucial to avoid system failure. The early detection of such a failure can help the industries in many ways. In this study, an effective methodology is demonstrated to diagnose the working condition of a three-phase induction motor using only the motor current signature and convolutional neural network (CNN). The electrical current signals are collected for five different types of fault conditions and one healthy condition of the induction motors. The proposed methodology mainly consists of two parts. The first part of the methodology demonstrates the preprocessing techniques, in which the time-series data signals are converted into two-dimensional (2-D) images. Two pattern recognition techniques based on recurrence plots (RPs) and empirical wavelet transform (EWT) are studied, to transform the raw one-dimensional (1-D) time-series current signals into 2-D RP and EWT images. The second part of the methodology explains how the proposed CNN automatically extracts the robust features from the images to diagnose the faults in the induction motors. The proposed methodology is studied using the dataset collected from different 3-phase induction motors working with different failure modes on full load conditions. The experimental results of the proposed methodology achieve competitive performance over traditional and other machine/deep learning methodologies. The CNN can be extensively applied to study the fault diagnosis of induction motors using the RPs and EWT images as a new feature extraction technique for time-series input signals such as current, vibration, temperature, and acoustics.

[1] Albrecht, P. Appiarius, J. McCoy, R. Owen, E. Sharma, D. Assessment of the Reliability of Motors in Utility Applications—Updated. IEEE Trans.Energy Convers. 1986.
[2] Bonnett, A.H. Soukup, G.C. Cause and Analysis of Stator and Rotor Failures in Three-Phase Squirrel-Cage Induction Motors. IEEE Trans. Ind. Appl. 1992.
[3] Dai, X. Gao, Z. From model signal to knowledge: A data-driven perspective of fault detection and diagnosis. IEEE Trans. Ind. Inform. 2013.
[4] Gao, Z. Cecati, C. Ding, S.X. A survey of fault diagnosis and fault-tolerant techniques-Part I: Fault diagnosis with model-based and signal-based approaches. IEEE Trans. Ind. Electron. 2015.
[5] Cecati, C. A survey of fault diagnosis and fault-tolerant techniques-Part II: Fault diagnosis with knowledge-based and hybrid/active approaches. IEEE Trans. Ind. Electron. 2015.
[6] He, D. Li, R. Zhu, J. Plastic bearing fault diagnosis based on a two step data mining approach. IEEE Trans.Ind. Electron. 2013.
[7] Seshadrinath, J. Singh, B. Panigrahi, B.K. Vibration Analysis Based Interturn Fault Diagnosis in Induction Machines. IEEE Trans. Ind. Inform. 2014.
[8] Gao, Z. Cecati, C. Ding, S. A Survey of Fault Diagnosis and Fault-Tolerant Techniques Part II: Fault Diagnosis with Knowledge-Based and Hybrid/Active Approaches. IEEE Trans. Ind. Electron. 2015.
[9] Zhang, X. Auxiliary Signal Design in Fault Detection and Diagnosis Springer: New York, NY, USA, 1989.
[10] Kerestecioglu, F. Change Detection and Input Design in Dynamic Systems Research Studies Press: Taunton, UK, 1993.
[11] Campbell, S.L. Nikoukhah, R. Auxiliary Signal Design for Failure Detection Walter de Gruyter GmbH: Berlin, Germany, 2004.
[12] Scott, J.K. Findeisen, R. Braatz, R.D. Raimondo, D.M. Input design for guaranteed fault diagnosis using zonotopes. Automatica 2014.
[13] Lee, J. Wu, F. Zhao, W. Ghaffari, M. Liao, L. Siegel, D. Prognostics and health management design for rotary machinery systems—Reviews, methodology, and applications. Mech. Syst. Signal Process. 2014.
[14] Lamim Filho, P.C.M. Pederiva, R. Brito, J.N. Detection of stator winding faults in induction machines using flux and vibration analysis. Mech. Syst. Signal Process, 42, 377–387, 2014.
[15] Sun, W. Chen, J. Li, J. Decision tree and PCA-based fault diagnosis of rotating machinery. Mech. Syst. Signal Process. 2007.
[16] Ngaopitakkul, A. Bunjongjit, S. An application of a discrete wavelet transform and a back-propagation neural network algorithm for fault diagnosis on single-circuit transmission line. Int. J. Syst. Sci. 2013.
[17] Yang, Y. Yu, D. Cheng, J. A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM. Measurements 2007.
[18] Pandya, D. Upadhyay, S. Harsha, S. Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN. Expert Syst. Appl. 2013.
[19] Lecun, Y. Bengio, Y. Hinton, G. Deep learning. Nature, 521, 436–444, 2015.
[20] Qi, Y. Shen, C.Wang, D. Shi, J. Jiang, X. Zhu, Z. Stacked Sparse Autoencoder-Based Deep Network for Fault Diagnosis of Rotating Machinery. IEEE Access, 5, 15066–15079 2017.
[21] Xia, M. Li, T. Xu, L. Liu, L. De Silva, C.W. Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks. IEEE/ASME Trans. Mechatron. 2018.
[22] Wen, L. Gao, L. Li, X. A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis. IEEE Trans. Syst. Man Cybern. Syst. 2017.
[23] Shao, H. Jiang, H. Zhang, X. Niu, M. Rolling bearing fault diagnosis using an optimization deep belief network. Meas. Sci. Technol. 2015.
[24] Lei, Y. Jia, F. Lin, J. Xing, S. Ding, S.X. An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data. IEEE Trans. Ind. Electron. 2016.
[25] Shao, H. Jiang, H. Wang, F. Zhao, H. An enhancement deep feature fusion method for rotating machinery fault diagnosis. Knowl. Based Syst. 2017.
[26] Lee, K.B. Cheon, S. Kim, C.O. A Convolutional Neural Network for Fault Classification and Diagnosis in Semiconductor Manufacturing Processes. IEEE Trans. Semicond. Manuf. 2017.
[27] Wang, J. Liu, P. She, M.F. Nahavandi, S. Kouzani, A. Bag-of-words representation for biomedical time series classification. Biomed. Signal Process. Control, pp 8, 634–644, 2013.
[28] Hatami, N. Chira, C. Hatami, N. Classifiers with a Reject Option for Early Time-Series Classification. In Proceedings of the 2013 IEEE Symposium on Computational Intelligence and Ensemble Learning (CIEL), Singapore, 16–19 April 2013.
[29] Wang, Z. Oates, T. Pooling Sax-Bop Approaches with Boosting to Classify Multivariate Synchronous Physio-Logical Time-Series Data. In Proceedings of the FLAIRS Conference, Hollywood, FL, USA, pp. 335–341, 18–20 May 2015.
[30] Ince, T. Kiranyaz, S. Eren, L. Askar, M. Gabbouj, M. Real-time motor fault detection by 1-D convolutional neural networks. IEEE Trans. Ind. Electron, 63, 7067–7075, 2016.
[31] A. Bouzida, O. Touhami, R. Ibtiouen, A. Belouchrani, M. Fadel, and A. Rezzoug, “Fault Diagnosis in Industrial Induction Machines Through Discrete Wavelet Transform,” IEEE Trans. Ind. Electron., vol. 58, no. 9, pp. 4385–4395, 2011
[32] J. Cusidó, L. Romeral, J. A. Ortega, J. A. Rosero, and A. G. Espinosa, “Fault Detection in Induction Machines Using Power Spectral Density in Wavelet Decomposition,” IEEE Trans. Ind. Electron., vol. 55, no. 2, pp. 633–643, 2008
[33] X. Lou and K. A. Loparo, “Bearing Fault Diagnosis Based On Wavelet Transform and Fuzzy Inference,” Mech. Syst. Signal Process., vol. 18, pp. 1077–1095, 2004.
[34] M. El and H. Benbouzid, “A Review of Induction Motors Signature Analysis as a Medium for Faults Detection,” IEEE Trans. Ind. Electron., vol. 47, no. 5, pp. 984–993, 2000.
[35] E. G. Strangas, S. Aviyente, and S. S. H. Zaidi, “Time – Frequency Analysis for Efficient Fault Diagnosis and Failure Prognosis for Interior Permanent-Magnet AC Motors,” IEEE Trans. Ind. Electron., vol. 55, no. 12, pp. 4191–4199, 2008.
[36] W. T. Thomson and M. Fenger, “Current Signature Analysis to Detect Induction Motor Faults,” IEEE Industry Applications Magazine, no. August, 2001.
[37] G. Georgoulas, T. Loutas, C. D. Stylios, and V. Kostopoulos, “Bearing Fault Detection Based On Hybrid Ensemble Detector and Empirical Mode Decomposition,” Mech. Syst. Signal Process., vol. 41, no. 1–2, pp. 510–525, 2013.
[38] Y. Lei, J. Lin, Z. He, and M. J. Zuo, “A Review On Empirical Mode Decomposition in Fault Diagnosis of Rotating Machinery,” Mech. Syst. Signal Process., vol. 35, no. 1–2, pp. 108–126, 2013.
[39] X. Zhang, Y. Liang, and J. Zhou, “A Novel Bearing Fault Diagnosis Model Integrated Permutation Entropy, Ensemble Empirical Mode Decomposition and Optimized Svm,” MEASUREMENT, vol. 69, pp. 164–179, 2015.
[40] A. G. Espinosa, J. A. Rosero, J. Cusid, L. Romeral, and J. A. Ortega, “Fault Detection by Means of Hilbert – Huang Transform of the Stator Current in a PMSM with Demagnetization,” IEEE Trans. ENERGY Convers., vol. 25, no. 2, pp. 312–318, 2010.
[41] X. Yu, E. Ding, C. Chen, X. Liu, and L. Li, “A Novel Characteristic Frequency Bands Extraction Method for Automatic Bearing Fault Diagnosis Based on Hilbert Huang Transform,” Sensors, pp. 27869–27893, 2015.
[42] V. K. Rai and A. R. Ã. Mohanty, “Bearing Fault Diagnosis Using FFT of Intrinsic Mode Functions in Hilbert – Huang Transform,” Mech. Syst. Signal Process., vol. 21, pp. 2607–2615, 2007.
[43] P. Konar and P. Chattopadhyay, “Multi-Class Fault Diagnosis of Induction Motor Using Hilbert and Wavelet Transform,” Appl. Soft Comput. J., vol. 30, pp. 341–352, 2015.
[44] D. Wang, Q. Miao, X. Fan, and H. Huang, “Rolling Element Bearing Fault Detection Using an Improved Combination of Hilbert and Wavelet Transforms,” J. Mech. Sci. Technol., vol. 23, pp. 3292–3301, 2009.
[45] Lee, H. Largman, Y. Pham, P. Ng, A. Unsupervised Feature Learning for Audio Classification Using Convolutional Deep Belief Networks. In Proceedings of the Conference on Neural Information Processing Systems (NIPS09), Vancouver, BC, Canada, 7–10, pp. 1096–1104, December 2009.
[46] Abdel-Hamid, O. Deng, L. Dong, Y. Exploring Convolutional Neural Network Structures and Optimization Techniques for Speech Recognition. In Proceedings of the 14th Annual Conference of the International Speech Communication Association (Interspeech 2013), Lyon, France, 25–29 August 2013.
[47] Wang, Z. Oates, T. Imaging Time-Series to Improve Classification and Imputation. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Buenos Aires, Argentina, pp. 3939–3945, 25–31 July 2015.
[48] Wang, Z. Oates, T. Encoding Time-Series as Images for Visual Inspection and Classification Using Tiled Convolutional Neural Networks. In Proceedings of the Association for the Advancement of Artificial Intelligence (AAAI) Conference, Austin, TX, USA, 25–30 January 2015.
[49] N. Marwan, M. C. Romano, M. Thiel, and J. Kurths, “Recurrence Plots for The Analysis of Complex Systems,” Phys. Rep., vol. 438, pp. 237–329, 2007.
[50] N. Marwan, “Historical Review of Recurrence Plots,” Eur. Phys. J. Spec. Top., pp. 1–11, 2008.
[51] 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. Mech. Syst. Signal Process. 2016.
[52] Abdeljaber, O. Avci, O. Kiranyaz, S. Gabbouj, M. Inman, D.J. Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. J. Sound Vib, 388, 154–170, 2017.
[53] Yang, J. Nguyen, M. San, P. Li, X. Krishnaswamy, S. Deep Convolutional Neural Networks on Multichannel Time-Series for Human Activity Recognition. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Buenos Aires, Argentina, pp. 3995–4001, 25–31 July 2015.
[54] Ma, F. Zhan, L. Li, C. Li, Z. Wang, T. Self-Adaptive Fault Feature Extraction of Rolling Bearings Based on Enhancing Mode Characteristic of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise. Symmetry 2019.
[55] Ge, M.Wang, J. Xu, Y. Zhang, F. Bai, K. Ren, X. Rolling Bearing Fault Diagnosis Based on EWT Sub-Modal Hypothesis Test and Ambiguity Correlation Classification. Symmetry 2018.
[56] Deng,W. Zhao, H. Yang, X. Dong, C.AFault Feature Extraction Method for Motor Bearing and Transmission Analysis. Symmetry 2017.
[57] Agrawal, P. Jayaswal, P. Diagnosis and Classifications of Bearing Faults Using Artificial Neural Network and Support Vector Machine. J. Inst. Eng. India Ser. C 2019.
[58] Jayaswal, P. Wadhwani, A.K. Application of artificial neural networks, fuzzy logic and wavelet transform in fault diagnosis via vibration signal analysis: A review. Aust. J. Mech. Eng. 2015.
[59] Bin, G.F. Gao, J.J. Li, X.J. Dhillon, B.S. Early fault diagnosis of rotating machinery based on wavelet packets-Empirical mode decomposition feature extraction and neural network. Mech. Syst. Signal Process. 27, 696–711, 2012.
[60] Chang, H. Kuo, C. Hsueh, Y.Wang, Y. Hsieh, C. Fuzzy-Based Fault Diagnosis System for Induction Motors on Smart Grid Structures. In Proceedings of the 2017 IEEE International Conference on Smart Energy Grid Engineering (SEGE), Oshawa, ON, Canada, 14–17, pp. 103–109, August 2017.
[61] D. Morinigo-sotelo, O. Duque-perez, and M. Perez-alonso, “Practical Aspects of Mixed-Eccentricity Detection in PWM Voltage-Source-Inverter-Fed Induction Motors,” IEEE Trans. Ind. Electron., vol. 57, no. 1, pp. 252–262, 2010
[62] G. Didier, E. Ternisien, O. Caspary, and H. Razik, “Fault Detection of Broken Rotor Bars in Induction Motor Using a Global Fault Index,” IEEE Trans. Ind. Appl., vol. 42, no. 1, pp. 79–88, 2006.
[63] A. M. Knight and S. P. Bertani, “Mechanical Fault Detection in a Medium-Sized Induction Motor Using Stator Current Monitoring,” IEEE Trans. ENERGY Convers., vol. 20, no. 4, pp. 753–760, 2005.
[64] Eckmann, J. Kamphorst, S. Ruelle, D. Recurrence plots of dynamical systems. EPL Euro Phys. Lett. 1987.
[65] Debayle, J. Hatami, N. Gavet, Y. Classification of Time-Series Images Using Deep Convolutional Neural Networks. In Proceedings of the Tenth International Conference on Machine Vision (ICMV 2017), Vienna, Austria, 13–15 November 2017.
[66] Huang, N.E. Shen, Z. Long, S.R. Wu, M.C. Shih, H.H. Zheng, Q. Yen, N.-C. Tung, C.C. Liu, H.H. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 1971.
[67] Torres, M.E. Colominas, M.A. Schlotthauer, G. Flandrin, P. A complete ensemble empirical mode decomposition with adaptive noise. In Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, pp. 4144–4147, 22–27 May 2011.
[68] Jerome, G. EmpiricalWavelet Transform. IEEE Trans. Signal Process. 2013.
[69] Huang, S.J. Hsieh, C.T. High-impedance fault detection utilizing a Morlet wavelet transform approach. IEEE Trans. Power Deliv. 1999.
[70] Lin, J. Qu, L. Feature extraction based on morlet wavelet and its application for mechanical fault diagnosis. J. Sound Vib. 2000.
[71] Peng, Z.K. Chu, F.L. Application of the wavelet transform in machine condition monitoring and fault diagnostics: A review with bibliography. Mech. Syst. Signal Process. 2004.
[72] Verstraete, D. Ferrada, A. Droguett, E.L. Meruane, V. Modarres, M. Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings. Shock Vib. 2017.
[73] Epirical wavelt transformation using Python. Available online: https://pypi.org/project/ewtpy/ (accessed on 12 May 2019).
[74] 2D plotting library in Python https://matplotlib.org/ (accessed on 12 May 2019).
[75] Krizhevsky, A. Sutskever, I. Hinton, GE. ImageNet classification with deep convolutional neural networks, Proceedings of the Conference on Neural Information Processing Systems (NIPS12), 2012.
[76] Bing Xu, Naiyan Wang, Tianqi Chen, Mu Li, “Empirical Evaluation of Rectified Activations in Convolutional Network”, last revised 27 Nov 2015.
[77] F. Chollet and others, “Keras.” 2015.
[78] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011.
[79] D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” pp. 1–15, 2014.
[80] Z. Zhang and M. R. Sabuncu, “Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels,” no. NeurIPS, 2018.
[81] Guo, X. Chen, L. Shen, C. Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis. Measurement 2016.