A nonparametric fault isolation approach through hybrid novelty score

Isolating the variable or set of variables responsible for an out-of-control signal is a challenging task in multivariate statistical process control. Several fault isolation approaches have been proposed. However, all assumed a multivariate normal distribution on the process data, an assumption that limits their applicability in many situations. In the present study we propose a nonparametric fault isolation approach based on a hybrid novelty score (HNS). A simulation study was conducted to examine the performance of our proposed HNS-based fault isolation approach, and its results were compared with both parametric and nonparametric T² decomposition approaches. The performance of our approach was superior in the simulation to either parametric or nonparametric T² decompositions. This was especially true in nonnormal situations.