A geometric framework for monitoring and fault detection for periodic processes

Abstract

Although cyclical operation systems are relatively widespread in practice (notably in the realm of physical separations, for example, pressure‐swing adsorption and chromatography), the development of specific fault detection mechanisms has received little attention compared to the extensive efforts dedicated to continuous or batch processes. Here, a novel geometric approach for process fault detection is proposed. Specifically, a time‐explicit multivariable representation of data collected from the process, which provides a natural framework for defining “normal” operation and the corresponding confidence regions is developed. On this basis, a two‐step fault detection approach is proposed, based on detecting intercycle variations to locate a faulty cycle, and intracycle changes to determine the exact timing of a fault. The theoretical developments are illustrated with two simulation case studies. © 2017 American Institute of Chemical Engineers AIChE J, 63: 2719–2730, 2017

Data Visualization and Visualization-Based Fault Detection for Chemical Processes

“…In the following sections, we describe the definition of the normal operating region and the associated fault detection approaches for three common types of processes: continuous, batch and periodic. The approaches described below are based on previous work by the authors [13][14][15].…”

“…Temperature (K) x 13 Generated heat (cal) x 14 Acid flow rate (mL/h) x 15 Base flow rate (mL/h) x 16 Cooling/heating water flow rate (L/h)…”

“…Motivated by this, in our past work [13][14][15], we introduced a new framework, time-explicit Kiviat diagrams, as a class of multivariate plots with an explicit time dimension. In this paper, we review the development of these diagrams and discuss fault detection applications of time-explicit Kiviat diagrams to three common types of chemical processes: continuous, batch and periodic processes.…”

“…These similarities allowed us to develop [15] a fault detection mechanism for periodic processes that relies on the concepts presented above for continuous and batch processes. Specifically, we divide the fault detection activity into two steps: a inter-cycle fault detection step that uses the oscillatory steady state to identify problematic cycles and an intra-cycle fault detection step that identifies where in the problematic cycles the deviation occurs.…”

“…In the inter-cycle step, we define a feature called the cyclic centroid [15] that characterizes a full cycle of the process in the aggregate. Since there are multiple cycles in the process, multiple cyclic centroids are obtained from the data.…”