F-8 DFBW sensor failure identification using analytic redundancy

Deckert, J. C.; Desai, Mihir A.; Deyst, John; Willsky, Alan S.

doi:10.1109/tac.1977.1101598

Cited by 209 publications

(51 citation statements)

References 16 publications

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“…The initial impetus for this approach came from the work reported in Deckert et al (1977) and Deckert (1981), in the context of aircraft failure detection. The noteworthy feature of that project was that the dynamics of the aircraft were decomposed in order to analyze the relative reliability of each individual source of potentially useful failure detection information.…”

Section: Introductionmentioning

confidence: 99%

“…In Chow and Willsky (1984) the results of initial attempts to extract the essence of the method used in Deckert et al (1977) and Deckert (1981) were presented in order to develop a general approach to robust failure detection. As discussed in those references and in others (Lou, 1982;Hall, 1981;Potter and Suman, 19771, all failure detection systems are based on exploiting analytical redundancy relations or (generalized) parity checks.…”

Section: Introductionmentioning

confidence: 99%

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Optimally robust redundancy relations for failure detection in uncertain systems

Lou¹,

Willsky²,

Verghese³

1986

Automatica

332

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A geometric interpretation of the concept of analytical redundancy leads to computationally simple procedures, involving singular value decompositions, Jor determining redundancy relations that are maximally insensitive to model uncertainties.Key Words--Failure detection; robustness; model reduction; least-squares approximation; linear systems.Al~traet--All failure detection methods are based, either explicitly or implicitly, on the use of redundancy, i.e. on (possibly dynamic) relations among the measured variables. The robustness of the failure detection process consequently depends to a great degree on the reliability of the redundancy relations, which in turn is affected by the inevitable presence of model uncertainties. In this paper the problem of determining redundancy relations that are optimally robust is addressed in a sense that includes several major issues of importance in practical failure detection and that provides a significant amount of intuition concerning the geometry of robust failure detection. A procedure is given involving the construction of a single matrix and its singular value decomposition for the determination of a complete sequence of redundancy relations, ordered in terms of their level of robustness. This procedure also provides the basis for comparing levels of robustness in redundancy provided by different sets of sensors.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimally robust redundancy relations for failure detection in uncertain systems

Lou¹,

Willsky²,

Verghese³

1986

Automatica

332

View full text Add to dashboard Cite

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“…[5,13]. The advantage of such an approach is that it allows one to separate the information provided by redundancy relations of differing levels of reliability, something that is not easily done when one starts with the overall model (2.1), (2.2) which combines all redundancy relations.…”

Section: Redundancy Relationsmentioning

confidence: 99%

“…The initial impetus for our approach to this problem came from the work reported in [5,13] which document the first and to date by far most successful application and flight testing of a failure detection algorithm based on advanced methods which use analytic redundancy. The singular feature of that project was that the dynamics of the aircraft were decomposed in order to analyze the relative reliability of each individual source of potentially useful failure detection information.…”

Section: Introductionmentioning

confidence: 99%

“…In [2] we presented the results of our initial attempt to extract the essence of the method used in [5,13] in order to develop a general approach to robust failure detection. As discussed in that reference and in others (such as [3,[7][8][9]), all failure detection systems are based on exploiting analytical redundancy relations or (generalized) parity checks.…”

Section: Introductionmentioning

confidence: 99%

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Redundancy relations and robust failure detection

Chow

Lou

Verghese

et al.

Detection of Abrupt Changes in Signals and Dynamical Systems

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All failure detection methods are based on the use of redundancy, that is on (possible dynamic) relations among the measured variables.Consequently the robustness of the failure detection process depends to a great degree on the reliability of the redundancy relations given the inevitable presence of model uncertainties.In this paper we address the problem of determining redundancy relations which are optimally robust in a sense which includes the major issues of importance in practical failure detection and which provides us with a significant amount of intuition concerning the geometry of robust failure detection.

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Data‐driven bounded‐error fault detection

Fábrega

Bravo²,

Herrera

et al. 2013

Adaptive Control & Signal

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In this paper, a new data-driven fault-detection method is proposed. This method is based on a new nonparametric system identification approach, which constitutes the principal contribution to this work. The fault-detection method is a parametric model-free approach that can be applied to nonlinear systems that work at various operating points. Not only can the fault-detection process be applied to the steady state of each operating point, but it can also be applied to the transient state resulting from a change in the operating point. In order to detect faults, the proposed method uses an interval predictor based on bounded-error techniques. The utilization of techniques based on bounded error enables system uncertainties to be included in an explicit way. This in turn leads to the possibility of obtaining interval predictions of the behaviour of the system, which include information on the reliability of the prediction itself. In order to show the effectiveness of the fault-detection method, two examples are presented: in the form of a simulated process (counter-flow shell-and-tube heat-exchanger system) and an example of a real application (two-tanks system). A comparison with two fault-detection methods has also been included.This model can be obtained in several different ways, such as using the physical knowledge of the system or by conventional identification methods. Model-based methods (MBMs) can be grouped into a few approaches: signal-processing methods [14], observer-based methods [15,16], parityrelation methods [17,18], parameter-estimation methods [19,20] and Kalman filter-based methods [21,22].The fault-detection method proposed in this work is parametric model free, so this previous knowledge about the system is not necessary. On the other hand, data-driven fault-detection methods use large volumes of data collected from industrial processes. These methods use an inference system based on statistical techniques [23] or by machine learning [9] applied to the historical data available. The most commonly used statistical techniques are principal component analysis (PCA) [12,24], partial least squares (PLS) [12], Fisher's discriminant analysis [25] and independent component analysis [26]. In general, these methods assume a linear relation in the studied variables of the system. To apply these statistical techniques to nonlinear systems, the corresponding nonlinear versions of the statistical techniques should be used: PCA [27,28] and PLS [5,29]. These nonlinear methods are usually related to a nonlinear optimization problem, and an iterative search algorithm is needed to be applied. These iterative search algorithms can suffer from convergence problems and can be overly sensitive to small data fluctuations [12]. The fault-detection method proposed in this work can be used for nonlinear systems; however, an iterative search algorithm is not needed.The most widely used artificial intelligence techniques include neural networks [30], expert systems [31], support vector machines [32], case-based reasoning...

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F-8 DFBW sensor failure identification using analytic redundancy

Cited by 209 publications

References 16 publications

Optimally robust redundancy relations for failure detection in uncertain systems

Optimally robust redundancy relations for failure detection in uncertain systems

Redundancy relations and robust failure detection

Data‐driven bounded‐error fault detection

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