Diagnosis of process faults in chemical systems using a local partial least squares approach

This article discusses the application of partial least squares (PLS) for monitoring complex chemical systems. In relation to existing work, this article proposes the integration of the statistical local approach into the PLS framework to monitor changes in the underlying model rather than analyzing the recorded input/output data directly. As discussed in the literature, monitoring changes in model parameters addresses the problems of nonstationary behavior and presents an analogy to model‐based approaches. The benefits of the proposed technique are that (i) a detailed mechanistic plant model is not required, (ii) nonstationary process behavior does not produce false alarms, (iii) parameter changes can be non‐Gaussian, (iv) Gaussian monitoring statistics can be established to simplify the monitoring task, and (v) fault magnitude and signatures can be estimated. This is demonstrated by a simulation example and the analysis of recorded data from two chemical processes.