Fault Diagnostics and Faulty Pattern Analysis of High-Speed Roller Bearings Using Deep Convolutional Neural Network

Abstract

In this article, vibration-based fault diagnostics and response classification have been done for defective high-speed cylindrical bearing operating under unbalance rotor conditions. An experimental study has been performed to capture the vibration signature of faulty bearings in the time domain and for different speeds of the unbalanced rotor. Two-dimensional phase trajectories are generated by estimating the time delay and embedding dimension corresponding to vibration signatures. Qualitative analysis involves the implementation of a deep convolutional neural network (DCNN) utilizing the phase portraits as input to classify the nonlinear vibration responses. Comparison with the state-of-art classifiers such as artificial neural network (ANN), deep neural network (DNN), and k-nearest neighbor (KNN) is presented based on classification accuracy values. Thus, the values obtained are 61%, 67%, 72%, and 99% for ANN, DNN, KNN, and DCNN, respectively. Hence, the proposed intelligent classification model accurately identifies the dynamic behavior of bearing under unbalanced rotor conditions.