Model-based fault detection and isolation requires a mathematical
model of the system behaviour. Modelling is important and can be
difficult because of the complexity of the monitored system and its
control architecture. The authors use bond-graph modelling, a
unified multi-energy domain modelling method, to build dynamic
models of process engineering systems by composing hierarchically
arranged sub-models of various commonly encountered process
engineering devices. The structural and causal properties of
bond-graph models are exploited for supervisory systems design.
The structural properties of a system, necessary for process
control, are elegantly derived from bond-graph models by following
the simple algorithms presented here. Additionally, structural
analysis of the model augmented with available instrumentation
indicates directly whether it is possible to detect and/or isolate
faults in some specific sub-space of the process. Such analysis
aids in the design and resource optimization of new supervision
platforms.
Static and dynamic constraints, which link the time evolution of
the known variables under normal operation, are evaluated in real
time to determine faults in the system. Various decision or
post-processing steps integral to the supervisory environment are
discussed in this monograph; they are required to extract
meaningful data from process state knowledge because of unavoidable
process uncertainties. Process state knowledge has been further
used to take active and passive fault accommodation measures.
Several applications to academic and small-scale-industrial
processes are interwoven throughout. Finally, an application
concerning development of asupervision platform for an industrial
plant is presented with experimental validation.
Model-based Process Supervision provides control engineers and
workers in industrial and academic research establishments
interested in process engineering with a means to build up a
practical and functional supervisory control environment and to use
sophisticated models to get the best use out of their process
data.
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