Journal
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
Volume 9, Issue 2, Pages 357-367Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/87.911387
Keywords
detection of abrupt changes; fault diagnosis; least squares methods; maximum likelihood detection; singular value decomposition
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This paper is concerned with detection of parameter changes of total least squares and generalized total least squares models and its application in fault detection and isolation, Total least squares and generalized total least squares are frequently used to model processes when all measured process variables are corrupted by disturbances. It is therefore of practical interest to monitor processes and detect faults using the total least squares and generalized total least squares as well. The local approach for detection of abrupt changes is adopted in this paper asa computational engine far the change detection. The effectiveness and robustness of the proposed algorithm in fault detection and isolation are demonstrated through Monte Carlo simulations, a pilot-scale experiment and sensor validation of an industrial distillation column.
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