Fault isolation during transient conditions on a heated two-tank system: a multiway PCA approach
Abstract
This study aimed to employ a Multiway Principal Component Analysis (MPCA)
approach for fault detection and isolation during transient conditions on a benchmark
heated two-tank system, which closely resembles a chemical process. The
significance lies in the system’s similarity to chemical processes, characterised by
controllable variables, noise, disturbances, and changes in operating modes. The
data generated by the system can include both faulty system data and data under
Normal Operating Conditions (NOC). Building on prior studies that focused on
fault detection and isolation during steady state conditions, this research employed
the MPCA approach to enhance the ability to detect and isolate faults during transient
conditions. For the model’s training, essential to Fault Detection and Isolation
(FDI), training data are generated from both a simulated model of the system
and the practical system. The simulated model’s data are then validated against the
practical system data. Following the model’s training, the MPCA approach, validated
through relevant literature, is applied to detect and isolate faults using test
data. This test data are generated from both the simulated model and the practical
system. Performance metrics, including detection rate, false alarm rate, time until
detection, and contribution plots, are employed to evaluate the effectiveness of the
MPCA method. Results indicate that the MPCA method successfully detected and
isolated faults using the SPE and T2 control charts for most faults, provided the
right minimum number of experimental and simulated training runs under NOC.
Due to extended run times in practical experiments, a combination of practical and
simulated data were utilized to achieve effective fault detection and isolation. In
conclusion, the research affirms the feasibility of detecting and isolating faults in
the benchmark heated two-tank system using the MPCA method during transient
conditions.
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