Bearing fault diagnosis based on improved variational mode decomposition and optimized stacked denoising autoencoder

It is difficult to extract the fault features of rolling bearings under noise interference. Aiming at this problem, a new feature extraction method based on improved Variational Mode Decomposition (VMD.) and Composite Zoom Permutation Entropy (CZPE) was proposed, and the optimized Stacked Denoising Auto-Encoders (SDAE) was used for fault classification. An improved VMD method was proposed to adaptivcly optimize the decomposition parameters by the new comprehensive evaluation index of cosine similarity-kurtosis-cnvclopc entropy , and the decomposed Intrinsic Mode Function (IMF) were screened by this index. To extract more comprehensive fault features, a new composite scaling permutation entropy was introduced to quantify the fault features of each effective IMF. A hybrid algorithm based on Rat Swarm Optimization (RSO) and Sparrow Search Algorithm (SSA) was proposed to optimize the hypcrparamctcrs of SDAE network, and the fault features were input into the optimized SDAE network to obtain the classification results. The American CWRU bearing data set was used for verification. The experimental results showed that the method could extract the fault features under the background of noise comprehensively and stably, and had better anti-noise performance and higher fault diagnosis accuracy than other methods. © 2024 CIMS. All rights reserved.