Fault diagnosis based on SPBO-SDAE and transformer neural network for rotating machinery

Abstract

Fault diagnosis for rotating machinery requires both high diagnosis accuracy and time efficiency. A rotating machinery fault diagnosis method based on intelligent feature self-extraction and transformer neural network is proposed. Firstly, the proposed method employs the student psychology based optimization (SPBO) algorithm to adaptively select hyper parameters, including the number of hidden layer nodes, sparsity coefficient and input data zeroing ratio, of the denoising auto encoder (DAE) network to determine the optimal structure of the stacked denoising auto encoders (SDAE) network. Secondly, the optimized SPBO-SDAE network is used to extract features from high-dimensional original data layer by layer. On this basis, the weight parameters of self-extracted features of SPBO-SDAE network are optimized through the self-attention mechanism of transformer deep neural network. The target features are retained, and the redundant features are filtered. Finally, in order to further validate the performance of the proposed model in the complex conditions, by adding Gaussian noise to the original data, the diagnosis performance of the proposed method is verified through four open data sets. The simulation results indicate that compared with the existing common shallow learning and deep learning methods, the proposed method has great advantages in generalization performance, fault diagnosis accuracy and time efficiency.