A fault diagnosis method for key transmission components of rotating machinery based on SAM-1DCNN-BiLSTM temporal and spatial feature extraction

Abstract

Bearing and reducer are the key transmission components of rotating machinery, timely and accurate fault diagnosis is an important guarantee for the safe operation of rotating machinery transmission system. The collected signals of bearing and reducer have typical non-stationary and nonlinear characteristics. In order to make full use of the spatial and temporal features contained in the running signals, combining with bidirectional Long short-term memory network, this paper proposes a one-dimensional convolutional neural network fault diagnosis model SAM-1DCNN-BiLSTM (Spatial attention model 1DCNN BiLSTM) based on attention mechanism with no pooling layer. Specifically, omitting pooling layer, the model uses spatial attention model (SAM) weighted 1DCNN network to extract spatial features, then, the BiLSTM layer is following to extract the time series features, and finally the device running state features that integrate spatial and temporal feature information are obtained, and the intelligent fault identification of key transmission components of rotating machinery are conducted. The performance of the developed method is verified with the experiments of the key components of bearing and reducer. For bearings, the average diagnostic accuracy reaches 99.77% with a variance of 0.03. For reducer, the average diagnostic accuracy reaches 99.00% with a variance of 0.11. Compared with the state-of-the-art deep learning diagnostic models, the proposed SAM-1DCNN-BiLSTM achieves better diagnostic performance.