A deep learning method for bearing fault diagnosis based on Cyclic Spectral Coherence and Convolutional Neural Networks. (June 2020) - Chen, Zhuyun; Mauricio, Alexandre; Li, Weihua; Gryllias, Konstantinos
Highlights: A novel scheme, combining Cyclic Spectral Coherence (CSCoh) and Convolutional Neural Networks (CNN), is proposed for rolling element bearing fault diagnosis, which is able to obtain improved classification performance. CSCoh is adopted to preprocess the vibration signals, which provides superior discriminative feature representations of bearing health statuses under different operating conditions. Group Normalization (GN) is developed to reduce the internal covariant shift induced by data distribution discrepancy, which extends the applications of the algorithm to real industrial environments. Abstract: Accurate fault diagnosis is critical to ensure the safe and reliable operation of rotating machinery. Data-driven fault diagnosis techniques based on Deep Learning (DL) have recently gained increasing attention due to theirs powerful feature learning capacity. However, one of the critical challenges lies in how to embed domain diagnosis knowledge into DL to obtain suitable features that correlate well with the health conditions and to generate better predictors. In this paper, a novel DL-based fault diagnosis method, based on 2D map representations of Cyclic Spectral Coherence (CSCoh) and Convolutional Neural Networks (CNN), is proposed to improve the recognition performance of rolling element bearing faults. Firstly, the 2D CSCoh maps of vibration signals are estimated by cyclic spectral analysis to provide bearing discriminative patterns for specific type of faults. The motivation for using CSCoh-based preprocessing scheme is that the valuable health condition information can be revealed by exploiting the second-order cyclostationary behavior of bearing vibration signals. Thus, the difficulty of feature learning in deep diagnosis model is reduced by leveraging domain-related diagnosis knowledge. Secondly, a CNN model is constructed to learn high-level feature representations and conduct fault classification. More specifically, Group Normalization (GN) is employed in CNN to normalize the feature maps of network, which can reduce the internal covariant shift induced by data distribution discrepancy. The proposed method is tested and evaluated on two experimental datasets, including data category imbalances and data collected under different operating conditions. Experimental results demonstrate that the proposed method can achieve high diagnosis accuracy under different datasets and present better generalization ability, compared to state-of-the-art fault diagnosis techniques.