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.