| Summary: | Fault detection and isolation (FDI) has become a crucial issue for industrial process
monitoring in order to increase availability, reliability, and production safety. Model-based FDI
methods rely on the mathematical model and input-output data of a process to perform
detection. The local approach is a new model-based FDI method which aims to detect slight
changes of parametric properties of a system. This thesis mainly addresses to the application of
FDI using the local approach.
Robustness with respect to model uncertainties is an important issue for the local approach.
A new algorithm was proposed to recalculate threshold based on the original threshold and
covariance matrix of the estimated parameters in order to reduce false alarms due to the
estimation error of process parameters. A similar algorithm was also provided to recalculate
threshold to reduce fault alarms due to regular parameter fluctuations. As fault detection
algorithms are often applied to closed-loop data, closed-loop fault detection was also
investigated. Two methods were proposed to deal with the relevance between system input and
output data in closed-loop detection: the dimension reduction method and the indirect detection
method. The dimension reduction method uses a linear transformation to reduce the dimension
of the normalized residual so that the covariance matrix of the revised normalized residual has
full rank. The indirect detection method uses the closed-loop model to calculate the primary
residual and the normalized residual. By detecting the changes of the closed-loop parameters,
the method also detects the changes of the open-loop parameters. Simulation results show that
both of these methods can detect changes of every single parameters of a system. Industrial
data from a cross-direction (CD) control system in a paper machine was also used to assess the
applicability of the local approach. By dividing the CD databox into small sections, the
sensitivity of the detection algorithm was improved and the algorithm successfully detected
abrupt faults of a single actuator. However, incipient faults of a single actuator can not be
detected due to noise and inaccuracy of the process model. === Applied Science, Faculty of === Chemical and Biological Engineering, Department of === Graduate
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