Real-time broken rotor bar fault detection and classification by shallow 1D convolutional neural networks
- 36 Downloads
Application of advanced fault diagnosis and monitoring techniques allows more efficient, reliable and safe operation of many complex industrial systems. Recently, there has been a significant increase in application of various data-driven deep learning models for motor fault detection and diagnosis problems. Due to high computational complexity and large training dataset requirements of deep learning models, in this study, shallow and adaptive 1D convolutional neural networks (CNNs) are applied to real-time detection and classification of broken rotor bars in induction motors. As opposed to traditional fault diagnosis systems with separately designed feature extraction and classification blocks, the proposed system takes directly raw stator current signals as input and it can automatically learn optimal features with the proper training. The other advantages of the proposed approach are (1) its compact architecture configuration performing only 1D convolutions with a set of filters and subsampling, making it suitable for implementing with real-time circuit monitoring, (2) its requirement for a limited size of training dataset for efficient training of the classifier and (3) its cost-effective implementation. Effectiveness and feasibility of the proposed method is validated by applying it to real motor current data from an induction motor under full load.
KeywordsBroken rotor bar detection Induction motors Convolutional neural networks
The author would like to thank Prof. Levent Eren for providing the broken rotor bar dataset for the experiments and giving the permission to use it.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- 2.Filippetti F, Bellini A, Capolino GA (2013) Condition monitoring and diagnosis of rotor faults in induction machines: state of art and future perspectives. In: Proceedings of the IEEE WEMDCD, Paris, March 2013, pp 196–209Google Scholar
- 8.Li DZ, Wang W, Ismail F (2015) An enhanced bispectrum technique with auxiliary frequency injection for induction motor health condition monitoring. IEEE Trans Instrum Meas 67:2279–2287Google Scholar
- 11.Di Stefano R, Meo S, Scarano M (1994) Induction motor fault diagnostic via artificial neural network. In: Proceedings of the IEEE international symposium on industrial electronics (ISIE’ 94), Santiago, pp 220–225Google Scholar
- 24.Thomson WT, Fenger M (2001) Current signature analysis to detect induction motor faults. IEEE Trans IAS Mag 7(4):26–34Google Scholar
- 38.Singh H, Seera M, Abdullah MZ (2013) Detection and diagnosis of broken rotor bars and eccentricity faults in induction motors using the fuzzy min-max neural network. In: 2013 international joint conference on neural networks (IJCNN), August 2013, pp 1–5Google Scholar
- 43.Wei Z, Gaoliang P, Chuanhao L (2016) Bearings fault diagnosis based on convolutional neural networks with 2-d representation of vibration signals as input. In: International conference on mechatronics and mechanical engineering (ICMME 2016), Feb 2017, pp 1–5Google Scholar
- 48.Scherer D, Muller A, Behnke S (2010) Evaluation of pooling operations in convolutional architectures for object recognition. In: Proceedings of international conference on artificial neural networks, Thessaloniki, Greece, September 2010, pp 92–101Google Scholar
- 49.Krizhevsky A, Sutskever I, Hinton G (2012) Imagenet classification with deep convolutional neural networks. In: Proceedings of advances in neural information processing systems (NIPS), Lake Tahoe, December 2012, pp 1097–1105Google Scholar
- 51.Eren L, Cekic Y, Devaney M (2009) Broken rotor bar detection via wavelet packet decomposition of motor current. Int Rev Electr Eng 4:844–850Google Scholar
- 52.Farabet C, Poulet C, Han J, LeCun Y (2009) CNP: an FPGA-based processor for convolutional networks. In: Proceedings of international conference on field programmable logic and applications, Prague, September 2009, pp 32–37Google Scholar