基于动态核主元分析的大功率LED阵列动态光源在线状态观测与故障诊断

大功率LED阵列动态光源工作过程中光电热参数具有不确定性和时变时滞非线性特点，利用动态核主元分析方法（DKPCA）对大功率LED阵列动态光源进行在线状态观测与故障诊断能有效地捕捉观测数据的非线性和相关性特征，根据历史数据的主元特征计算出的统计量阈值和在线数据的统计特征实现故障检测，利用重构贡献图法实现故障的分离。仿真实验表明，对大功率LED阵列动态光源典型的传感器和执行器故障进行有效监测和诊断相对于核主元分析方法对故障更为敏感，故障检测率最高提高了7.5%，误检率下降了4.2%。

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%.