KPI relevant and irrelevant fault monitoring with neighborhood component analysis and two-level PLS

Abstract

Generally, the efficiency of key performance indicator (KPI) relevant and irrelevant fault monitoring is highly related to the definition of KPI relevant and irrelevant variations in the input space. Therefore, a novel KPI relevant and irrelevant fault monitoring scheme is proposed, the offline modeling phase of which incorporates a neighborhood component analysis (NCA)-based KPI relevant variable selection method and a two-level partial least square (PLS) modeling strategy. With the utilization of NCA, the KPI relevant and irrelevant input variables could be determined, respectively. To obtain an explicit decomposition of KPI relevant and irrelevant variations from the input, a two-level PLS modeling strategy is proposed to avoid the potential loss of KPI relevant information that hidden in KPI irrelevant variables and the potential loss of KPI irrelevant information that resulted from the first level PLS model. It is thus expected to achieve superior performance than the methods that considered in the current work. The effectiveness and superiority of the proposed method in monitoring KPI relevant and irrelevant faults have also be demonstrated by implementing comparisons with its counterparts.