Research on feature extraction and intelligent diagnosis method of reciprocating compressor bearing clearance fault - Feng Yan (Journal of Engineering and Applied Science)
Abstract This study presents an innovative approach to fault diagnosis in sliding bearings, targeting the challenges posed by weak fault signals and heavy noise interference. The proposed method employs a generalized multi-scale permutation entropy (GMPE) algorithm, which utilizes a multi-scale mean coarse-graining strategy to effectively capture dynamic transitions in signals. To overcome the shortcomings of traditional binary tree support vector machine (BTSVM) classifiers—such as slow convergence and error accumulation due to early misclassifications—an enhanced BTSVM model is introduced to reduce error propagation. The effectiveness of the method is validated on both reciprocating compressor sliding bearings and automotive rolling bearings, achieving a fault diagnosis accuracy of over 99%. These results highlight a significant advancement in mechanical fault detection and demonstrate the strong potential of combining GMPE with an improved BTSVM for accurate fault diagnosis in complex machinery.