On-line state observation and fault diagnosis of high-power LED array dynamic light source based on dynamic kernel principal component analysis

During the working process of high-power LED array dynamic light source, the photoelectric and thermal parameters have the characteristics of uncertainty and time-varying delay nonlinearity. The dynamic kernel principal component analysis method (DKPCA) was used to conduct the on-line state observation and fault diagnosis of the high-power LED array dynamic light source, which could effectively capture the nonlinearity and correlation characteristics of the observation data, realize the fault detection based on the statistical threshold value calculated by the principal component characteristics of the historical data and the statistical characteristics of the online data, and realize the separation of the fault by the reconstruction contribution graph method. The simulation experiments show that the effective monitoring and diagnosis of typical sensor and actuator faults of high-power LED array dynamic light source are more sensitive to faults than kernel principal component analysis method. The fault detection rate is increased by 7.5%, and the false detection rate is reduced by 4.2%.