Research on gearbox bearing fault diagnosis based on SSA-VMD-CNN algorithms

Abstract Gearbox bearings are crucial components in numerous mechanical systems. These gearboxes typically operate in environments characterized by significant noise, causing their fault signals to be obscured by background interference, vibrations, and signals from other mechanical parts. This interference complicates the accurate extraction and diagnosis of fault characteristics from complex data. To address this challenge, we propose a novel bearing fault diagnosis model that integrates Variational Mode Decomposition (VMD), Convolutional Neural Network (CNN), and advanced optimization algorithms. Initially, the Squirrel Search Algorithm (SSA) is employed to automatically optimize VMD parameters, enabling efficient extraction of denoised signal features. VMD decomposes vibration signals into multiple Intrinsic Mode Functions (IMFs), which are then analyzed and reconstructed using kurtosis and cross-correlation criteria. Subsequently, these processed signals serve as input feature vectors for the CNN model, facilitating both training and testing phases. The model is designed to construct a singular value vector matrix that reflects the current fault state based on the position of each submatrix. Simulation verification of our model demonstrates an accuracy exceeding 95% in bearing fault diagnosis, a substantial improvement over traditional methods. This advancement offers a new perspective for the health monitoring and maintenance of critical mechanical equipment, such as gearboxes. It holds significant potential for application in intelligent manufacturing and automated monitoring systems in the future.