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International Journal of Adaptive Control and Signal Processing
Article . 2018 . Peer-reviewed
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zbMATH Open
Article . 2018
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Detector design for active fault diagnosis in closed‐loop systems

Detector design for active fault diagnosis in closed-loop systems
Authors: A. K. Sekunda; H. H. Niemann; N. K. Poulsen;

Detector design for active fault diagnosis in closed‐loop systems

Abstract

SummaryFault diagnosis of closed‐loop systems is extremely relevant for high‐precision equipment and safety critical systems. Fault diagnosis is usually divided into 2 schemes: active and passive fault diagnosis. Recent studies have highlighted some advantages of active fault diagnosis based on dual Youla‐Jabr‐Bongiorno‐Kucera parameters. In this paper, a method for closed‐loop active fault diagnosis based on statistical detectors is given using dual Youla‐Jabr‐Bongiorno‐Kucera parameters. The goal of this paper is 2‐fold. First, the authors introduce a method for measuring a residual signal subject to white noise. Second, an optimal detector design is presented for single and multiple faults using the amplitude and phase shift of the residual signal to conduct diagnosis. Here, both the optimal case of a perfect model and the suboptimal case of a model with uncertainties are discussed. The method is successfully tested on a simulated system with parametric faults.

Country
Denmark
Related Organizations
Keywords

Safety engineering, Failure analysis, Multiple faults, Observability, Closed-loop systems, Control/observation systems with incomplete information, Safety critical systems, White noise, Simulated system, YJBK parameters, Closed loop systems, Statistical tests, statistical test, Active fault diagnosis, Parametric fault, Stochastic systems in control theory (general), Electrical and Electronic Engineering, Residual signals, Faulting, Feedback control, active fault diagnosis, Control and Systems Engineering, Linear systems in control theory, closed-loop systems, Signal Processing, Statistical test, Optimal detectors, Fault detection

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    popularity
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    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Top 10%
Average
Top 10%
Green
bronze