A generalized least-squares fault detection filter

Chen, Robert H.; Speyer, Jason L.

doi:10.1002/1099-1115(200011)14:7<747::aid-acs619>3.0.co;2-f

Cited by 38 publications

(27 citation statements)

References 4 publications

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“…Observers or filters are used to generate residuals, which can be analyzed and dealt with to detect and diagnose faults mainly where the min-max optimization techniques have been applied to the estimation error systems, in order to guarantee some of the required performances [7][8][9][10].…”

Section: Intelligent Methods For Industrial Monitoring Processes Conmentioning

confidence: 99%

Intelligent methods for process control and diagnostics of a mill fan system

Doukovska

Vassileva²

2014

Cybernetics and Information Technologies

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Section: Intelligent Methods For Industrial Monitoring Processes Conmentioning

confidence: 99%

Intelligent methods for process control and diagnostics of a mill fan system

Doukovska

Vassileva²

2014

Cybernetics and Information Technologies

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“…And for γ x k +1 , we denote (27) where Φ(·, ·, ·) is denoted by (3). With (25) and (24), the dynamics of estimation errors in the presence of δ k+1 (w k+1 ) and in the absence of w k+1 (δ k+1 ) can also be represented respectively by…”

Section: Simplified Calculation Of the Error Jpdfsmentioning

confidence: 99%

“…Fault detection (FD) has long been regarded as an important and integrated part in modern control systems for improving reliability [1][2][3][4][5][6][7][8][9][10]. The so-far obtained approaches include the system identification technique [2] and the statistic approaches based on the Bayesian theorem, likelihood methods and hypothesis test techniques [11].…”

Section: Introductionmentioning

confidence: 99%

Function Based Fault Detection for Uncertain Multivariate Nonlinear Non-Gaussian Stochastic Systems Using Entropy Optimization Principle

Yin

Zhou

2012

Entropy

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In this paper, the fault detection in uncertain multivariate nonlinear non-Gaussian stochastic systems is further investigated. Entropy is introduced to characterize the stochastic behavior of the detection errors, and the entropy optimization principle is established for the fault detection problem. The principle is to maximize the entropies of the stochastic detection errors in the presence of faults and to minimize the entropies of the detection errors in the presence of disturbances. In order to calculate the entropies, the formulations of the joint probability density functions (JPDFs) of the stochastic errors are presented in terms of the known JPDFs of both the disturbances and the faults. By using the novel performance indexes and the formulations for the entropies of the detection errors, new fault detection design methods are provided for the considered multivariate nonlinear non-Gaussian plants. Finally, a simulation example is given to illustrate the efficiency of the proposed fault detection algorithm.

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“…However, it was shown in [12] that the invariant zero directions are not automatically included in the detection spaces created by the optimization, though they can be included artificially by modifying the fault directions. The GTFDF was applied to decentralized fault detection in [13] and a similar detection filter with enhanced sensitivity to the fault to be detected was derived in [14]. In [15], [16], Mutuel extended the GTFDF to discrete-time systems.…”

Section: Introductionmentioning

confidence: 99%

A discrete-time game theoretic multiple-fault detection filter

Murray

Speyer²

2014

53rd IEEE Conference on Decision and Control

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In this paper, a previously published discretetime game theoretic fault detection filter is generalized to the multiple-fault case. The derivation models the detection filter problem as a collection of disturbance attenuation problems, which are optimized via a single cost function. Once solved, the implied discrete-time Riccati inequalities may be used as the constraints of a secondary cost function, which is optimized to find a specific filter gain that achieves secondary objectives. Furthermore, it is shown that the novel detection filter approximates those obtained using spectral and geometric theories, improves their robustness to noise and initial condition errors, and models sensor faults directly rather than requiring that they be rewritten as dynamic faults.

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A generalized least-squares fault detection filter

Cited by 38 publications

References 4 publications

Intelligent methods for process control and diagnostics of a mill fan system

Intelligent methods for process control and diagnostics of a mill fan system

Function Based Fault Detection for Uncertain Multivariate Nonlinear Non-Gaussian Stochastic Systems Using Entropy Optimization Principle

A discrete-time game theoretic multiple-fault detection filter

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