NGO-MRE combined with DELM for bearing fault diagnosis

To improve the effectiveness of Multiscale Range Entropy (MRE) in extracting features from rolling bearing faults, this paper proposes a novel bearing fault diagnosis method that combines the Northern Goshawk Optimization (NGO) algorithm, MRE, and a Deep Extreme Learning Machine (DELM). First, the raw vibration signal is decomposed using Variational Mode Decomposition (VMD) to obtain its Intrinsic Mode Functions (IMFs). Second, the NGO algorithm is employed to optimize the MRE parameters, using the minimum crest factor as the objective function. Then, using these optimized parameters, MRE is applied to extract fault features. The resulting feature set undergoes dimensionality reduction to create the final sample set. Finally, an NGO-optimized DELM is used for fault classification. The experimental results demonstrate that this method effectively extracts the characteristic signals of rolling bearing faults through robust parameter optimization, thereby improving the accuracy of fault diagnosis.