Physics-Based Convolutional Neural Network for Fault Diagnosis of Rolling Element Bearings

During the past few years, deep learning has been recognized as a useful tool in condition monitoring and fault detection of rolling element bearings. Although existing deep learning approaches are able to intelligently detect and classify the faults in bearings, they still face one or both of the following challenges: 1) most of these approaches rely exclusively on data and do not incorporate physical knowledge into the learning and prediction processes and 2) the approaches often focus on the fault diagnosis of a single bearing in a rotating machine, while in reality, a rotating machine may contain multiple bearings. To address these challenges, this paper proposes a novel approach, namely physics-based convolutional neural network (PCNN), for fault diagnosis of rolling element bearings. In PCNN, an exclusively data-driven deep learning approach, called CNN, is carefully modified to incorporate useful information from physical knowledge about bearings and their fault characteristics. To this end, the proposed approach 1) utilizes spectral kurtosis and envelope analysis to extract sidebands from raw sensor signals and minimize non-transient components of the signals and 2) feeds the information about the fault characteristics into the CNN model. With the capability to process signals from multiple sensors, the proposed PCNN approach is capable of concurrently monitoring multiple bearings and detecting faults in these bearings. The performance of PCNN in machinery fault diagnosis is compared with that of traditional machine learning- and deep learning-based approaches reported in the literature.