A fault diagnosis method for rolling bearings based on RDDAN under multivariable working conditions

Abstract

Abstract Mechanical equipment in actual motion can produce noise interference with the vibration signal of rolling bearings, which have non-constant loads and speed. These factors lead to variable and unstable vibration signals of rolling bearings; therefore, it is very difficult to accurately diagnose actual running rolling bearings. In this paper, a residual denoising dynamic adaptation network (RDDAN) is proposed, which uses the signal knowledge under known working conditions to diagnose rolling bearing faults under unknown working conditions. The method mainly consists of data preprocessing, feature extraction, and dynamic distribution adaptation. First, Gaussian noise is added at the data preprocessing stage to emulate the noise perturbation during the reality of rolling bearing operation. Secondly, a deep residual shrinkage network 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. In comparison with other models, the RDDAN improves the average accuracy by about 23%. The results show that the 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.