| dc.description.abstract | Fault detection and diagnosis have gained central importance in the chemical
process industries over the past decade. This is due to several reasons, one of them
being that copious amount of data is available from a large number of sensors in
process plants. Moreover, since industrial processes operate in closed loop with appropriate
output feedback to attain certain performance objectives, instrument faults
have a direct effect on the overall performance of the automation system. Extracting
essential information about the state of the system and processing the measurements
for detecting, discriminating, and identifying abnormal readings are important tasks
of a fault diagnosis system.
The goal of this dissertation is to develop such fault diagnosis systems, which
use limited information about the process model to robustly detect, discriminate, and
reconstruct instrumentation faults. Broadly, the proposed method consists of a novel
nonlinear state and parameter estimator coupled with a fault detection, discrimination,
and reconstruction system.
The first part of this dissertation focuses on designing fault diagnosis systems
that not only perform fault detection and isolation but also estimate the shape and
size of the unknown instrument faults. This notion is extended to nonlinear processes whose structure is known but the parameters of the process are a priori uncertain and
bounded. Since the uncertainty in the process model and instrument fault detection
interact with each other, a novel two-time scale procedure is adopted to render overall
fault diagnosis. Further, some techniques to enhance the convergence properties of
the proposed state and parameter estimator are presented.
The remaining part of the dissertation extends the proposed model-based fault
diagnosis methodology to processes for which first principles modeling is either expensive
or infeasible. This is achieved by using an empirical model identification
technique called subspace identification for state-space characterization of the process.
Finally the proposed methodology for fault diagnosis has been applied in numerical
simulations to a non-isothermal CSTR (continuous stirred tank reactor), an
industrial melter process, and a debutanizer plant. | en |
