Abstract Rolling bearings are essential for ensuring smooth operation of rotating machinery, when it suffer from faults, the extraction of fault features and fault diagnosis of rolling bearings become difficult due to the interference of noise and other vibration components. A fault diagnosis model based on symplectic geometric mode decomposition (SGMD), convolutional neural networks (CNN) and optimized long short-term memory networks (LSTM) is proposed to facilitate feature extraction and improve robustness of fault diagnosis. Firstly, bearing signals from different faults are decomposed using the SGMD, selecting the symplectic seometric components (SGCs) based on their correlation and constructing feature vectors by fuzzy dispersion entropy (FDE). Then, a CNN-LSTM model is built by combining the CNN and the LSTM. Inputting training data into the model to adjust its hyperparameters, and the softmax layer is replaced with a least squares support vector machine (LSSVM). The parameters of the LSSVM are optimized with the electric eel foraging optimization (EEFO) algorithm and the optimized model is used for fault identification and classification. Finally, experiments are conducted on two public datasets, achieving accuracy rates of 98.57% and 97.22% respectively, which validates the feasibility of this method and demonstrates its superiority over traditional CNN models.
Xu et al. (Thu,) studied this question.