Dynamic model-based techniques for the detection of incidents on freeways

Willsky, Alan S.; Chow, Edward; Gershwin, Stanley B.; Greene, C.; Houpt, Paul; Kurkjian, A.

doi:10.1109/tac.1980.1102392

Cited by 160 publications

(54 citation statements)

References 19 publications

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“…y(k) = Cq x(k) (12) where q indexes the models associated with different possible values of the unknown parameters. (13) This is the subspace in which the window of observations for the system (11), (12) lives, as x(k-s) varies over all possible values.…”

Section: A Geometric Approach To Robust Redundancymentioning

confidence: 99%

“…one obtains a variable w that is governed by a similarity-transformed version of (11), (12) and has "equally likely" excursions of "unit length" in each direction under the q-th model. This sort of normalization is discussed more at the end of this section and in Section 4.1, where observation and process noise are incorporated into the model.…”

Section: A Geometric Approach To Robust Redundancymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimally Robust Redundancy Relations for Failure Detection in Uncertain Systems

Lou¹,

Willsky²,

Verghese³

1985

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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 we address the problem of determining redundancy relations that are optimally robust, 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. We also give a procedure, 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.

show abstract

Section: A Geometric Approach To Robust Redundancymentioning

confidence: 99%

Section: A Geometric Approach To Robust Redundancymentioning

confidence: 99%

See 1 more Smart Citation

Optimally Robust Redundancy Relations for Failure Detection in Uncertain Systems

Lou¹,

Willsky²,

Verghese³

1985

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show abstract

“…GLR test has been proposed in order to establish an adaptive system, which reaches three important problems; estimation, fault detection and magnitude compensation of jumps. GLR test is proposed for fault detection of different applications: geophysical signal segmentation [9], signals and dynamic systems [10], incident fault detection on freeways [11], missiles trajectory [12]. Therefore, in the current work it is proposed to exploit the advantages of the GLR test for improved fault detection when a process model is not available.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Monitoring of Environmental Processes

Mansouri¹,

Destain²,

Nounou³

et al. 2016

IJESD

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Abstract-The process monitoring systems are often utilized in environmental process operations. Many practical applications used for scheduling, planning or operator training are often complex for direct usage in process monitoring. In this paper, it is proposed to use the generalized likelihood ratio (GLR) based principal components analysis (PCA) for process monitoring and fault detection of environmental processes. The objective is to combine the GLR test with PCA model in order to improve the fault detection performance. GLR-based PCA is a multivariate statistical technique used in multivariate statistical process monitoring and fault detection. PCA reduces the dimensionality of the original data by projecting it onto a space with significantly fewer dimensions. It obtains the principal events of variability in a process. If some of these events change, it can be due to a fault in the process. The data are collected from the crop model in order to calculate the PCA model and the thresholds; Hotelling statistic, 2 T , Q statistic and GLR test statistic are used in order to detect the faults. It is demonstrated that the performance of faults detection can be improved by combining GLR test and PCA.

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“…The work described here focuses on directly designing a failure detection system that is insensitive to model errors (rather than designing a system that attempts to compensate the detection algorithm by estimating uncertainties on-line, (Leininger, 1981;Hall, 1981;Willsky et al, 1980). 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.…”

Section: Introductionmentioning

confidence: 99%

Optimally robust redundancy relations for failure detection in uncertain systems

Lou¹,

Willsky²,

Verghese³

1986

Automatica

334

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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.

show abstract

Dynamic model-based techniques for the detection of incidents on freeways

Cited by 160 publications

References 19 publications

Optimally Robust Redundancy Relations for Failure Detection in Uncertain Systems

Optimally Robust Redundancy Relations for Failure Detection in Uncertain Systems

Enhanced Monitoring of Environmental Processes

Optimally robust redundancy relations for failure detection in uncertain systems

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