Rolling bearing performance degradation assessment based on singular value decomposition-sliding window linear regression and improved deep learning network in noisy environment

Abstract

It is difficult to evaluate the degradation performance and the degradation state of a rolling bearing in noisy environment. A new method is proposed to solve the problem based on singular value decomposition (SVD)-sliding window linear regression and ResNeXt–multi-attention mechanism’s deep neural network (RMADNN). Firstly, the root mean square (rms) gradient value is calculated on the basis of rms based on SVD and sliding window linear regression, which is used as the rolling bearing performance degradation indicator in noisy environment. Secondly, the degradation state of the rolling bearing is divided by the rms gradient. Thirdly, for the deep learning network model, the soft attention mechanism is introduced into the bidirectional long short-term memory network to extract more important and deep fault features. At the same time, the ResNeXt layer is added into the convolutional neural network to extract more fault features and merge them through multi-scale grouped convolution. The hybrid domain attention mechanism (HDAM) is introduced after the ResNeXt layer. The HDAM can screen out more important features from the output features of the ResNeXt in the two dimensions of channel and spatial which demonstrates the improved deep learning network of the RMADNN. Finally, the labeled data set is input into the improved model for training, and the Softmax classifier is used to identify the life degradation state of the rolling bearing. Through the verification of multiple test data sets, the results show that the method proposed in this research is very effective on the rolling bearing performance degradation assessment.