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.