A hybrid deep-learning model for fault diagnosis of rolling bearings in strong noise environments

Abstract Strong noise in practical engineering environments interferes with the signal of a rolling bearing, which leads to the decline of the diagnosis accuracy of intelligent diagnosis models. This paper proposes a novel hybrid model (a convolutional denoising auto-encoder (CDAE)-BLCNN) to address this problem. First, the rolling bearing vibration signal containing noise was input into the CDAE, which denoises the signal through unsupervised learning and then outputs the reconstructed data. Secondly, a hybrid neural network (BLCNN), composed of a multi-scale wide convolution neural network and a bidirectional long-short-term memory network, 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 levels and various rotating speeds. Compared with other models, there is a high fault recognition rate, robustness, and generalization ability, which may be favorable to practical applications.