An Evolving Fuzzy Belief Classification Technology for Bearing Fault Diagnosis

Self-organizing fuzzy belief inference systems (SOFBISs) have been used in evolving prototype-based classifications. However, the existing SOFBIS models treat all features and statistical descriptors equally, which may limit their performance in applications where input features have unequal diagnostic relevance. This paper presents an evolving fuzzy belief (EFB) technique to enhance pattern classification and machine condition monitoring. Application of a new feature, Manhattan distance measure, is proposed to highlight informative features and suppress less relevant patterns. The feature weights are estimated using class-wise Pearson correlations to emphasize informative features, thereby improving the interpretability and accuracy of fault diagnosis. The effectiveness of the proposed EFB technique for bearing health condition monitoring and fault diagnosis is examined by both simulation and experimental tests.