A hybrid deep-learning model for fault diagnosis of rolling bearings under strong noise environment
Abstract Aiming at the problem that the signal of rolling bearing is interfered by strong noise in practical engineering environment, which leads to the decline of the diagnosis accuracy of intelligent diagnosis model. This paper proposes a novel hybrid model (CDAE-BLCNN). First, the rolling bearing vibration signal containing noise was input into the Convolutional Denoising Auto-Encoder (CDAE), which denoises the signal through unsupervised learning, and then outputs the reconstructed data. Secondly, a hybrid neural network (BLCNN) composed of multi-scale wide convolution kernel block (MWCNN) and bidirectional long-short-term memory network (BiLSTM) was used to extract intrinsic fault features from the reconstructed signal and diagnose fault types. The analysis results demonstrate that the proposed hybrid deep learning model achieves higher detection accuracy even under different noise and various rotating speed. Compared with other models, there is a high fault recognition rate, robustness, and generalization ability, which may be favorable to practical applications.