Advanced fault diagnostics in rotating machinery: A deep learning approach using octave theory of dynamic vibration signals

Abstract

In engineering practice, the diagnosis of rotating machinery faults often faces numerous challenges, including noise interference and changes in operating conditions, which pose new difficulties for deep learning methods lacking prior knowledge. In response to this issue, this paper proposes a rotating machinery fault diagnosis method (OCML) based on octave, convolutional neural networks, and MOGRIFIER LSTM. This method can simply and effectively achieve noise reduction, feature extraction, and classification. Firstly, through octave analysis, noise and redundant information can be conveniently and effectively filtered out, enhancing the signal feature representation. Secondly, the designed CNN-MOGRIFIER LSTM model can effectively capture local features and temporal dependencies in the data and has good information interaction capabilities. Experiments on the CWRU dataset and the permanent magnet synchronous motor fault dataset demonstrate that the proposed method exhibits good diagnostic performance across different fault scenarios and different operating conditions. Furthermore, compared to other rotating machinery fault diagnosis methods, the proposed OCML method performs better in terms of diagnostic accuracy and stability. These results collectively confirm the good generalization of the proposed method.