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Robust multiple-fault detection filter
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DOI：10.1002/rnc.715.png)
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Abstract
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A new robust multiple-fault detection and identification algorithm is determined. Different from other algorithms which explicitly force the geometric structure by using eigenstructure assignment or geometric theory, this algorithm is derived from solving an optimization problem. The output error is divided into several subspaces. For each subspace, the transmission from one fault, denoted the associated target fault, is maximized while the transmission from other faults, denoted the associated nuisance fault, is minimized. Therefore, each projected residual of the robust multiple-fault detection filter is affected primarily by one fault and minimally by other faults. The transmission from process and sensor noises is also minimized so that the filter is robust with respect to these disturbances. It is shown that, in the limit where the weighting on each associated nuisance fault transmission goes to infinity, the filter recovers the geometric structure of the restricted diagonal detection filter of which the Beard-Jones detection filter and unknown input observer are special cases. Filter designs can be obtained for both time-invariant and time-varying systems. Copyright (C) 2002 John Wiley Sons, Ltd.
Keywords:
fault detection and identification
analytical redundancy
Beard-Jones detection filter
approximate fault detection filter
robust fault detection filter
time-varying system
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3.2
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