Optimized fault detection in bearings of rotating machines via batch normalization-integrated bidirectional gated recurrent unit networks

Abstract

Motor is commonly used in industrial applications. Although motors are frequently found to have bearing problems, this causes a serious safety risk to industrial production. Traditionally, fault diagnostics methods often required only signal processing techniques and are ineffective. To overcome this problem, deep learning (DL) has been recently developed rapidly and achieved remarkable results in fault diagnosis. The intelligent fault diagnosis and classification of rolling bearing faults based on ensemble empirical mode decomposition (EEMD) and batch normalization (BN), principal component analysis (PCA) based stacked bidirectional-gated recurrent unit (Bi-GRU) neural network, is proposed in this paper. BN is introduced to improve the fast convergence of gated recurrent unit (GRU). EEMD is applied to eliminate the noise interference from the vibrational signal, and then important features are selected using the correlation coefficient value. Next, PCA is utilized for dimensionality reduction to retain only the essential. Finally, the BN based stacked Bi-GRU model is developed to classify faults based on extracted features. The proposed model correctly classifies the different types of faults in real operating conditions and also compared with existing techniques.