Identification of Sensor Fault Characteristics Based on Adaptive Kernel Principal Component Analysis

Abstract

With the ongoing advancement of industrial automation towards higher levels of intelligence, such as increased use of machine learning and artificial intelligence for process optimization, multidimensional monitoring and real-time fault diagnosis of complex industrial processes have emerged as critical challenges for ensuring the reliable operation of production systems and the consistent quality of products. Among multivariate statistical process monitoring approaches, conventional Principal Component Analysis (PCA) is inherently constrained by its linear projection mechanism, leading to significant performance degradation when addressing process data exhibiting nonlinear characteristics. To overcome this limitation, this study proposes an Adaptive Kernel Principal Component Analysis (AKPCA) method based on kernel space mapping. By utilizing Mercer kernel functions, the original process data is nonlinearly mapped into a Reproducing Kernel Hilbert Space (RKHS), thereby enhancing the separability of nonlinear features. Furthermore, a two-tier fault diagnosis framework is established: the first tier employs an adaptive KPCA model integrated with a sliding window mechanism for fault detection, while the second tier utilizes a Contribution Analysis (CA) algorithm for fault source identification. To validate the robustness of the proposed method, we simulate four representative types of faults — bias faults, complete failures, offset faults, and precision degradation. Experimental results substantiate that the adaptive KPCA approach not only accurately detects faults but also effectively localizes fault sources through contribution analysis.