Fault detection of nonlinear processes based on switching linear regression models

In recent years several statistical methods have been applied to condition monitoring of various processes under linearity and stationarity assumptions. However most of the actual industrial processes, e.g. in the chemical sector, are strongly nonlinear. Furthermore the hypothesis of data Gaussian distribution does not often hold, thus causing a decrease of the fault detection accuracy. In this paper a Switching Linear Regression (SLR) approach is firstly proposed in a fault detection scenario. The basic idea is to estimate different Linear Regression models through an arbitrary clustering algorithm and then switching among these models. The developed algorithm allows to deal with nonlinear processes. The proposed fault detection approach is applied to two simulated test bench and on the Tennessee Eastman process benchmark. Furthermore, compared with the Linear Regression algorithm, SLR shows better performance in terms of fault detection accuracy.