A KPI-related multiplicative fault diagnosis scheme for industrial processes

Abstract

In this paper, a key performance indicator (KPI) related multiplicative fault diagnosis scheme is proposed for static industrial processes. This scheme is developed for an alternative algorithm to the standard partial least squares (PLS) based process monitoring, where no design parameter like “latent variable number” is involved. Based on both normal and faulty data sets, the multiplicative fault information is firstly estimated. With this knowledge, the most critical low-level control loop/component is further identified. Different from the existing data-driven additive fault diagnosis approaches, this scheme aims to handle the second order statistics, which is of fatal importance for KPI-related fault diagnosis. Finally, an academic example is investigated to illustrate the functionality of this scheme.