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This study addresses the randomness of training parameters in the Deep Belief Network (DBN) and proposes an optimization method for rolling bearing fault diagnosis based on the Sparrow Search Algorithm (SSA). SSA is employed to globally optimize the structural and training parameters of the DBN network, effectively resolving the challenge of parameter determination. Simultaneously, vibration signals are extracted from multiple dimensions to capture different types of fault features. These features are derived through Wavelet Transformation (WT) for noise reduction and Intrinsic Mode Functions (IMFs) extraction through Ensemble Empirical Mode Decomposition (EEMD). The fusion of time-domain and frequency-domain dimensional features forms a multidimensional feature set. This comprehensive feature set optimizes the parameters of the deep learning network and significantly improves the accuracy and effectiveness of rolling bearing fault diagnosis. With a remarkable recognition accuracy of 99.17%, this approach outperforms conventional feature sets and mainstream diagnostic methods such as PSO-DBN and SSA-SVM while maintaining high levels of generalization and stability. The introduction of this method represents a significant breakthrough in the field of rolling bearing fault diagnosis.

Optimization of Deep Belief Network Based on Sparrow Search Algorithm for Rolling Bearing Fault Diagnosis