Modeling in the observable or latent space? A comparison of dynamic latent variable based monitoring methods for sensor fault detection

Abstract

Latent variable approaches are among the state-of-the-art methodologies for industrial process monitoring. Their robustness, ease of implementation, theoretical maturity, and computational efficiency make them privileged candidates for industrial adoption, when compared to other alternatives. As a ubiquitous feature of industrial processes, system dynamics is incorporated in latent variable frameworks in two distinct ways: explicitly, in terms of the observed variables, or implicitly, in the latent variables domain. This modeling aspect has been absent from discussion in the technical literature, but ends up having an impact in the monitoring performance. In this work, we focus our analysis in two state-of-the-art dynamic latent variable classes of models, that typify each modeling perspective: dynamic principal component analysis with decorrelated residuals (DPCA-DR) and Dynamic-Inner Canonical Correlation Analysis (DiCCA). The benchmark system consists of a Biodiesel production unit, where process degradation also takes place and several types of faults are realistically simulated (code publicly available). We have identified some poorly known limitations of these state-of-the-art methods, such as the reduced sensitivity due to fault adaptation and their total inability to handle integrating systems. Furthermore, the results obtained, complemented with a theoretical analysis of the two methods, robustly point to the existence of an advantage of using DPCA-DR for detecting sensor faults.