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 the 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 linear regression of sliding window, which is used as the rolling bearing performance degradation indicator in noisy environment. Secondly, the degradation state of rolling bearing is divided by the RMS gradient. Thirdly, for the part of the deep learning network model, the soft attention mechanism is introduced into the bidirectional long short-term memory network (BiLSTM) to extract more important and deep fault features. At the same time, the ResNeXt layer is added into the convolutional neural network (CNN) to extract more fault features and merge them through multi-scale grouped convolution. Then, the hybrid domain attention mechanism (HDAM) was 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. Therefore, the improved deep learning network of the ResNeXt - multi-attention mechanism's deep neural network (RMADNN) in this research is established. Finally, the labeled data set is input into the improved model for training, and the Softmax classifier is used to identify the life decline state of the rolling bearing. The result shows that the indicator of RMS gradient proposed has a better characterization, and the RMADNN model can distinguish the life degradation state of rolling bearing better.

Gearbox Fault Diagnosis Based on Gramian Angular Field and CSKD-ResNeXt

“…On the basis of the residual structure, ResNeXt proposes a new dimension of cardinality and uses group convolution [55] to replace the three-layer convolution structure of ResNet, which not only improves the accuracy of the neural network but also reduces the parameter complexity so that ResNeXt performs better in neural network models with the same complexity. In addition, based on the ResNet structure of ResNeXt, the idea of parallel topology is introduced to increase the cardinality to 32, as shown in Figure 2.…”

Bearing performance degradation assessment using adversarial fusion convolutional autoencoder based on multi-source information

“…Yan et al (2022b) implemented the embedded hidden Markov model (EHMM) to fuse three kinds of features into the global model. Dong et al (2022) introduced the hybrid domain attention mechanism (HDAM) after the ResNeXt layer which can screen out more important features. Xin et al (2022) utilized a comprehensive index reduction and SVDD evaluation model to characterize and evaluate the rolling bearing lifetime degradation process.…”