A self-adaptive DRSN-GPReLU for bearing fault diagnosis under variable working conditions

Recently, deep learning has been widely used for intelligent fault diagnosis of rolling bearings due to its no-mankind feature extraction capability. The majority of intelligent diagnosis methods are based on the assumption that the data collected is from constant working conditions. However, rolling bearings often operate under variable working conditions in the real diagnosis scenario, which reduces the generalization capability of the diagnosis model. To solve this problem, a self-adaptive deep residual shrinkage network with a global parametric rectifier linear unit (DRSN-GPReLU) is proposed in this paper. First, the DRSN is used as the basic architecture to improve the anti-noise ability of the proposed method. Then, a novel activation function-the GPReLU-is developed, which can achieve better intra-class compactness for vibration signals, and the inter-class samples are better mapped into remote areas. Finally, a sub-network based on the attention mechanism is designed to automatically infer the slope of the GPReLU. Various experimental results demonstrate that the DRSN-GPReLU can realize better performance compared with traditional methods under variable working conditions, and has better robustness under noise interference.