Intelligent Fault Diagnosis of Rotating Machines Based on Wavelet Time-Frequency Diagram and Optimized Stacked Denoising Auto-Encoder

When a stacked denoising auto-encoder (SDAE) manually sets several parameters, the gradient of neuron weight becomes dispersed, reducing the ability to retrieve sensitive fault feature information from a bearing vibration signal under multiple working conditions and strong noise. A bearing health monitoring and defect diagnostic model based on variational mode decomposition (VMD) combined with continuous wavelet transform (CWT) and SDAE optimized by sparrow search algorithm (SSA) is presented to tackle this problem. The wavelet time-frequency diagram is obtained by VMD and CWT, which maps the fault characteristic information to different local positions in time and scale, and then the wavelet time-frequency diagrams are input into the SDAE for in-depth training. To achieve the ideal structure of SDAE and increase the feature extraction capabilities of SDAE for weak signals, SSA is utilized for the global combination and adaptive selection of several SDAE parameters. The bearing failure diagnostic model based on VMD-CWT-SSA-SDAE outperforms BPNN, SVM, the traditional SDAE, GA-SDAE, PSO-SDAE, and SSA-DBN in diagnosis accuracy, generalization performance, and anti-noise performance when tested on various data sets.