Research on gearbox fault diagnosis based on the CHO-VMD-BiGRU algorithm

To address the challenges of difficult feature extraction from gearbox fault signals and low accuracy in fault diagnosis, this paper presents a novel diagnostic model that integrates Variational Mode Decomposition (VMD) optimized by the Chaotic Hippopotamus Optimization algorithm (CHO) with a Bidirectional Gated Recurrent Unit (BiGRU) neural network. Firstly, CHO is used to optimize the number of modal decomposition and the penalty factor of VMD, using a composite index that combines the ratio of permutation entropy (PE) to mutual information entropy (MIE) as the fitness function. Then, the optimized VMD method decomposes the signal, and nine statistical features are extracted from the optimal Intrinsic Mode Function (IMF)components to form feature vectors. Finally, the feature vectors are input into the BiGRU for fault recognition. Experimental results demonstrate that the proposed model integrating CHO-VMD-BiGRU, can accurately extract fault features and outperform other algorithms, showing promising practical application value.