Fault Detection Schemes for Continuous-Time Stochastic Dynamical Systems

Castillo, Ángela; Zufiria, Pedro J.

doi:10.1109/tac.2009.2024568

Cited by 28 publications

(37 citation statements)

References 25 publications

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“…A CUSUM-type approximation algorithm has also been proposed which makes use of the log-likelihood functions into a simplifying sliding window scheme. The unified setting, successfully gathers, depending on different types of simplifications, the existing detection schemes for continuous-time stochastic dynamical systems [12][13][14][41][42][43]50]. This general formulation allows for the definition of new detection schemes and for a comparative analysis among them.…”

Section: Discussionmentioning

confidence: 99%

“…Applying a Luenberger observer-type consistency checker to the system, the following residual is obtained (see [12][13][14][41][42][43]46,50]): where the matrix A = diag(li,..., 1"), with X¡ > 0, i = 1,..., n is a design set of parameters; for the sake of simplicity A = X • I n with X > 0 multiplying all elements of the n x n identity matrix I" will be considered. Under such assumptions, the residual vector process e(t) defined via the MS integral (or Ito integral for generalized processes) does exist, it is sample continuous with continuous mean and variance-covariance matrix, and it is formed by Gaussian components [33].…”

Section: Problem Statementmentioning

confidence: 99%

“…Alternatively, in [12][13][14] different detection schemes are considered componentwise, named /x a (t) = e(t), /x b (t) =\ f a e{x)dx, and fi c {t) =\ f tT e{x)dx, under the framework of constant mean estimation. These schemes can also be seen as simplifications of (12) where LR is replaced by the corresponding approximations of the sufficient statistic (8), again for large values of X:…”

Section: Unification Of Previous Schemes Proposed In the Literaturementioning

confidence: 99%

“…We apply the sliding window scheme proposed in (12) with ¡t of (8) as a sufficient estimator measure. Different values of the window size land the drift parameter 1 are considered in order to compare this scheme (based on ¡t) with the detection schemes proposed in [13,14] (t), to provide an good trade-of between robustness against false alarms and sensitivity to faults. This behavior is typical in a wide range of values of 1 e [1,10].…”

Section: Simulation Examplementioning

confidence: 99%

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A mathematical framework for new fault detection schemes in nonlinear stochastic continuous-time dynamical systems

Zufiria

2012

Applied Mathematics and Computation

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In this work, a mathematical unifying framework for designing new fault detection schemes in nonlinear stochastic continuous-time dynamical systems is developed. These schemes are based on a stochastic process, called the residual, which reflects the system behavior and whose changes are to be detected. A quickest detection scheme for the residual is proposed, which is based on the computed likelihood ratios for time-varying statistical changes in the Ornstein-Uhlenbeck process. Several expressions are provided, depending on a priori knowledge of the fault, which can be employed in a proposed CUSUM-type approximated scheme. This general setting gathers different existing fault detection schemes within a unifying framework, and allows for the definition of new ones. A comparative simulation example illustrates the behavior of the proposed schemes.

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Section: Discussionmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

Section: Unification Of Previous Schemes Proposed In the Literaturementioning

confidence: 99%

Section: Simulation Examplementioning

confidence: 99%

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A mathematical framework for new fault detection schemes in nonlinear stochastic continuous-time dynamical systems

Zufiria

2012

Applied Mathematics and Computation

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“…The use of continuous-time stochastic models in system fault diagnosis provides a novel framework for taking into account system and sensor noises and disturbances, in order to construct new detection and isolation algorithms (Castillo et al, 2003;Castillo, 2006;Castillo and Zufiria, 2009;Münz and Zufiria, 2005;Münz and Zufiria, 2009). The seminal work in Castillo et al (2003) developed an initial study on both the detection and the isolation problems.…”

Section: Introductionmentioning

confidence: 99%

Fault isolation schemes for a class of continuous-time stochastic dynamical systems

Castillo¹,

Zufiria²

2013

Annual Reviews in Control

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In this paper a new method for fault isolation in a class of continuous-time stochastic dynamical systems is proposed. The method is framed in the context of model-based analytical redundancy, consisting in the generation of a residual signal by means of a diagnostic observer, for its posterior analysis. Once a fault has been detected, and assuming some basic a priori knowledge about the set of possible failures in the plant, the isolation task is then formulated as a type of on-line statistical classification problem. The proposed isolation scheme employs in parallel different hypotheses tests on a statistic of the residual signal, one test for each possible fault. This isolation method is characterized by deriving for the unidimensional case, a sufficient isolability condition as well as an upperbound of the probability of missed isolation. Simulation examples illustrate the applicability of the proposed scheme.

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Adaptive control and signal processing literature survey (No. 15)

2009

Adaptive Control & Signal

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In order to keep readers up to date, the journal will contain a list of recently published journal articles relevant to the fields of robust and nonlinear control six times a year. This list is compiled from a wide range of journals. Please note that even though the list has been broadly categorized these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please e-mail Leonardo Giovanini at lgiovanini@fich.unl.edu.ar. 2009; 57(9):3488-3497.Karafyllis I, Kravaris C. From continuous-time design to sampled-data design of observers. IEEE Transactions on

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Fault Detection Schemes for Continuous-Time Stochastic Dynamical Systems

Cited by 28 publications

References 25 publications

A mathematical framework for new fault detection schemes in nonlinear stochastic continuous-time dynamical systems

A mathematical framework for new fault detection schemes in nonlinear stochastic continuous-time dynamical systems

Fault isolation schemes for a class of continuous-time stochastic dynamical systems

Adaptive control and signal processing literature survey (No. 15)

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