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

摘要

Highlights?An unsupervised feature extraction method based on GKPCA is proposed for nonlinear deterioration process under multi no-label signals.?Taking the first component as deterioration feature of complex equipment in system-level is appropriate.?The more exact deterioration features with better performance are identified from GKPCA-PC1.?The unsupervised feature extraction method is more effective and efficiency for deterioration level identification and health management.AbstractThe 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 engin