Fault detection and diagnosis in chemical processes using sensitive principal component analysis

Sensitive principal component analysis (SPCA) is proposed to improve the principal component analysis (PCA) based chemical process monitoring performance, by solving the information loss problem and reducing nondetection rates of the T-2 statistic. Generally, principal components (PCs) selection in the PCA-based process monitoring is subjective, which can lead to information loss and poor monitoring performance. The SPCA method is to subsequently build a conventional PCA model based on normal samples, index PCs which reflect the dominant variation of abnormal observations, and use these sensitive PCs (SPCs) to monitor the process. Moreover, a novel fault diagnosis approach based on SPCA is also proposed due to SPCs' ability to represent the main characteristic of the fault. The case studies on the Tennessee Eastman process demonstrate the effect of SPCA on online monitoring, showing its performance is significantly better than that of the classical PCA methods.