Research on Rolling Bearing Fault Diagnosis Method Based on MPE and Multi-Strategy Improved Sparrow Search Algorithm Under Local Mean Decomposition

To address the issues of non-stationarity, noise interference, and insufficient discriminative power of traditional fault feature extraction methods in rolling bearing vibration signals, this paper proposes a fault diagnosis method based on multi-scale permutation entropy (MPE) and a multi-strategy improved sparrow search algorithm (MSSA) under local mean decomposition (LMD). First, LMD is employed to adaptively decompose the original signal. Effective product functions (PFs) are then selected using the Pearson correlation coefficient, enabling signal reconstruction that suppresses noise interference while preserving fault impact components. Second, to overcome the limited capability of traditional time-frequency features in representing complex fault patterns, MPE is introduced to construct a multi-scale complexity feature vector, effectively capturing the scale-dependent differences in the dynamic behavior of signals. Furthermore, considering the instability of classification caused by the empirical setting of hidden layer nodes in the extreme learning machine (ELM), a multi-strategy improved sparrow search algorithm is proposed to optimize ELM parameters. This algorithm integrates an adaptive Levy flight mechanism and dynamic reverse learning. The long-tail jump characteristics of Levy flight enhance the global search capability, while dynamic reverse learning increases population diversity, preventing premature convergence. The experimental results demonstrate that the proposed method achieves an average diagnostic accuracy of over 96% across multiple datasets, verifying its robustness in handling non-stationary signals and fault classification.