Real-time broken rotor bar fault detection and classification by shallow 1D convolutional neural networks

Abstract

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.