A hybrid deep-learning model for fault diagnosis of rolling bearings in strong noise environments
Ke Zhang, Caizi Fan, Xiaochen Zhang, Huaitao Shi, Songhua Li
Abstract
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.