Incipient fault diagnosis method of nonlinear chemical process based on weighted statistical local KPCA

The traditional local kernel principal component analysis (SLKPCA) does not consider the difference of samples when constructing the improved residual, so that the fault sample information is easily covered by other samples. This paper proposes a new fault diagnosis method of nonlinear chemical process based on weighted statistical local kernel principal component analysis (WSLKPCA). Firstly, the score vectors and the eigenvalues are obtained using KPCA and the residual function is constructed. Then, a weighting strategy based on the distance between the test sample and the training sample is designed to construct the weighting improved residual, which assigns larger weights to samples with strong incipient fault information to enhance the impact of fault samples. Finally, the contribution graph is constructed based on the weighted mutual information between the measured variables and monitoring statistics to identify the fault source variables. Simulation results on continuous stirred tank reactor and TE process show that the proposed method can effectively detect incipient faults, and has better fault recognition performance. © All Right Reserved.