An unsupervised feature extraction method for nonlinear deterioration process of complex equipment under multi dimensional no-label signals

The nonlinearity in complex equipment, such as turbofan engine and electric motor, is a substantial factor. The nature of complex equipment deterioration refers to a nonlinear deterioration process. An unsupervised feature extraction method based on greedy kernel principal components analysis (GKPCA) is proposed for nonlinear deterioration process under multi dimensional no-label signals. The 21 signals of turbofan engine and 6 signals of electric motor are analyzed using the method, respectively. The results show that: taking the first component as deterioration feature of complex equipment in system-level is appropriate; the more exact deterioration features with better performance in monotonicity, robustness and computation speed, are identified from GKPCA-PC1, thus the features can be used for online monitoring and warning of complex equipment; the unsupervised feature extraction method considering nonlinearity is more effective and efficiency for deterioration level identification and health management such as adding lubricant grease at optical time to prevent Over-alarm. The results verify the effectiveness and efficiency of the method in dealing with nonlinearity in deterioration feature extraction of complex equipment. Through the study, it reveals that considering the substantial nonlinearity in complex equipment is important for extracting more exact deterioration feature under multi dimensional no-label signals.