Fault Detection and Accommodation in Particulate Processes with Sampled and Delayed Measurements

Abstract

A model-based framework is developed for component fault detection and accommodation in particulate processes with discretely sampled and delayed measurements. An observer-based output feedback controller is initially designed based on a suitable reduced-order model that captures the dominant process dynamics. The controller includes an intersample model predictor that compensates for measurement intermittency and a propagation unit that compensates for the delays. The intersample model predictor provides the observer with process output estimates between sensor measurements, and the model states are updated using current output estimates obtained from the propagation unit. The fault-free stability properties are characterized in terms of model accuracy, sampling rate, and delay size. These properties are used to derive appropriate rules for fault detection and accommodation. The difference between the output estimates from the state observer and the propagation unit is compared against a time-varying alarm threshold for fault detection. Once the threshold is breached, controller design parameters are adjusted to preserve closed-loop stability. The proposed methodology is illustrated using a continuous crystallizer example.