Fault detection in time-varying chemical process through incremental principal component analysis

Incremental principal component analysis (IPCA) is proposed to improve the detection performance of a slow ramp fault in the time-varying chemical process. Conventional monitoring methods of the time-varying process such as recursive method and moving window strategy, which update the monitoring model and control limit when the newly monitored sample is detected as a normal one, track the slow ramp fault and lose the ability to detect this kind of fault. In this study, the incremental principal components (IPCs) describing time-varying information are proposed to extract the normal time-varying information. This study proposes IPCA method based on IPCs for process monitoring of the time-varying processes. The monitoring model remained unchanged because the normal time-varying information has already been identified by IPCs. The method can distinguish between the slow ramp fault from the normal time-varying process. Two numeric case studies demonstrate the efficiency of the method. Application of the method to an acetylene hydrogenation reactor is also provided.