Fault diagnosis of complex hydraulic system based on fast Mahalanobis classification system with high-dimensional imbalanced data

Abstract

Due to the complexity of hydraulic systems, the data generated is prone to be high-dimensional and imbalanced, posing a challenge for fault diagnosis. This paper proposes a fast Mahalanobis classification system (FMCS). At the dimensionality reduction stage, smymetrical uncertainty and Mahalanobis kernel principal component analysis are used to construct a two-stage fast dimensionality reduction method. At the weight distribution stage, Mahalanobis distance is used to measure the similarity between the main feature and other features to determine the feature weights. At the fault diagnosis stage, Mahalanobis-Taguchi system is improved in threshold determination by atom search optimization to achieve fault diagnosis. Experiments show that FMCS has better classification performance (accuracy = 0.92, F1 = 0.94, G-mean = 0.91) and shorter computing time (4.9045 s) compared with 24 baseline algorithms. FMCS promotes the development of intelligent fault diagnosis for complex hydraulic systems.