Feature Extraction Using Hierarchical Dispersion Entropy for Rolling Bearing Fault Diagnosis

Effective feature extraction is crucial for accurate fault diagnosis of rolling bearings. A novel feature extraction method called hierarchical dispersion entropy (HDE) based on hierarchical analysis is proposed in this study. The proposed method includes the following three steps: 1) bearing vibration signal is decomposed into a series of subband signal components; 2) dispersion entropies of the components in different frequency bands are calculated as the original feature vector; and 3) joint approximate diagonalization of eigenmatrices (JADE) is used to extract fusion features from the original features. The main contributions of the proposed method are as follows: 1) the HDE method can characterize the complexity and uncertainty of the signal in full frequency band; 2) the JADE method further eliminates redundant information while greatly retaining fault-relevant information; and 3) the proposed method combines the advantages of HDE's hierarchical analysis and JADE's information fusion capabilities, so the fusion features extracted by the proposed method can be more effective for the establishment of fault pattern identification model. In the analysis of two experimental cases, the feature extracted by the proposed method shows better feature clustering effect and higher recognition rate than other methods. The results show that compared with other methods, the proposed method can more accurately characterize the health condition of the bearing.

Automated summary

The study introduces a novel feature extraction method called HDE, which integrates the advantages of hierarchical analysis and information fusion for more effective fault diagnosis of rolling bearings. Experimental results demonstrate that the features extracted by the proposed method exhibit better clustering effect and higher recognition rate compared to other methods.