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.

Mechanical equipment in actual motion can produce noise interference with the vibration signal of rolling bearings, which have non-constant load and speed. These factors lead to variable and unstable vibration signals of rolling bearings, so it is very difficult to accurately diagnose the actual running rolling bearings. In this paper, a Residual Denoising Dynamic Adaptive Network (RDDAN) is proposed, which uses the signal knowledge under known working conditions to diagnose the rolling bearing faults under unknown working conditions. The method mainly consists of data pre-processing, feature extraction, and dynamic distribution adaptation. First, Gaussian noise is added in the data pre-processing stage to emulate the noise perturbation in the reality of rolling bearing operation. Secondly, a Deep Residual Shrinkage Network (DRSN) is used for noise reduction and feature extraction. Finally, the marginal probability distribution and conditional probability distribution under different working conditions are calculated depending on the characteristics. The network is disciplined using the relative weight of the marginal probability distribution and the conditional probability distribution. And the fault classification results are output after multiple iterations. The method was tested on the Case Western Reserve University bearing dataset and the Machine Fault Simulator Magnum bearing dataset respectively. By comparing other models, RDDAN improves the average accuracy by about 23%. The results show that RDDAN can effectively solve the problem of inconsistent data distribution in rolling bearings under operating conditions influenced by multiple variables such as noise, load, and speed.

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