Improved key performance indicator-partial least squares method for nonlinear process fault detection based on just-in-time learning

Abstract

Key performance indicator-partial least squares (KPI-PLS) achieves linear indicator fault detection through the appropriate decomposition of variables into related and unrelated parts. This approach uses the projection matrix obtained by general singular value decomposition to decompose the residual space of PLS and thus achieves a more reasonable division of data space. However, KPI-PLS has limitations in handling nonlinear issues. Such problems can be effectively addressed by a local model, just-in-time learning (JITL). Therefore, in this study, an improved KPI-PLS method combined with JITL (JITL-KPI-PLS) is proposed to achieve nonlinear fault detection. The local model is built using hierarchical clustering to improve computational efficiency of JITL for searching relevant samples. By updating the model and control limit in real time, the current status of online samples can be better tracked. Finally, better fault detection performance of the proposed method is confirmed on the Tennessee Eastman benchmark and the Zhoushan thermal power plant.