Residual Generation for Fault Diagnosis of Systems Described by Linear Differential-Algebraic Equations

An algorithm is proposed for computing which sensor additions that make a diagnosis requirement specification regarding fault detectability and isolability attainable for a given linear differential-algebraic model. Restrictions on possible sensor locations can be given and if the diagnosis specification is not attainable with any available sensor addition, the algorithm provides the solutions that maximize specification fulfillment. Previous approaches with similar objectives have been structural, but since this algorithm is analytical, it can handle models where structural approaches fail. A Mathematica implementation of the algorithm can be downloaded from http://www.fs.isy.liu.se/Software/LinSensPlaceTool/.

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“…Although intuitive, a detectability condition, proved in [10], directly related to the model matrices are given next.…”

Section: Basic Results On Detectability and Isolabilitymentioning

confidence: 99%

“…The results in this section are primarily based on the presentation in [10] but equivalent results exist for other model descriptions.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Sensor Placement for Fault Isolation in Linear Differential-Algebraic Systems

Krysander

Frisk

Åslund

2008

IFAC Proceedings Volumes

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“…The definition of Z fi is a stochastic counterpart to observation sets in the deterministic case, see [24]. Each fault mode f i , including NF, can be described by a set Z fi .…”

Section: Stochastic Characterization Of Fault Modesmentioning

confidence: 99%

“…applying the transformation (19), 2. redefining the matrices L, H, F , and N given the transformed model fulfilling assumption (18), and 3. computing distinguishability using (24).…”

Section: Proofmentioning

confidence: 99%

A method for quantitative fault diagnosability analysis of stochastic linear descriptor models

2013

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Analyzing fault diagnosability performance for a given model, before developing a diagnosis algorithm, can be used to answer questions like "How difficult is it to detect a fault fi?" or "How difficult is it to isolate a fault fi from a fault fj?". The main contributions are the derivation of a measure, distinguishability, and a method for analyzing fault diagnosability performance of discrete-time descriptor models. The method, based on the Kullback-Leibler divergence, utilizes a stochastic characterization of the different fault modes to quantify diagnosability performance. Another contribution is the relation between distinguishability and the fault to noise ratio of residual generators. It is also shown how to design residual generators with maximum fault to noise ratio if the noise is assumed to be i.i.d. Gaussian signals. Finally, the method is applied to a heavy duty diesel engine model to exemplify how to analyze diagnosability performance of non-linear dynamic models.

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“…To determine which part, only the deterministic model needs to be considered. It can be shown [12] that residual r i checks the consistency of ξ i (q)w = 0 where ξ i (q) is a polynomial in the time-shift operator. By defining the set of consistent observations for tests in a similar way as for models, we define…”

Section: B Tests For Checking Model Consistencymentioning

confidence: 99%

Dynamic test selection for reconfigurable diagnosis

Krysander

Heintz

Roll

et al. 2008

2008 47th IEEE Conference on Decision and Control

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Abstract-Detecting and isolating multiple faults is a computationally intense task which typically consists of computing a set of tests, and then computing the diagnoses based on the test results. This paper proposes a method to reduce the computational burden by only running the tests that are currently needed, and dynamically starting new tests when the need changes. A main contribution is a method to select tests such that the computational burden is reduced while maintaining the isolation performance of the diagnostic system. Key components in the approach are the test selection algorithm, the test initialization procedures, and a knowledge processing framework that supports the functionality needed. The approach is exemplified on a relatively small dynamical system, which still illustrates the complexity and possible computational gain with the proposed approach. 1

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Residual Generation for Fault Diagnosis of Systems Described by Linear Differential-Algebraic Equations

Cited by 58 publications

References 17 publications

Sensor Placement for Fault Isolation in Linear Differential-Algebraic Systems

Sensor Placement for Fault Isolation in Linear Differential-Algebraic Systems

A method for quantitative fault diagnosability analysis of stochastic linear descriptor models

Dynamic test selection for reconfigurable diagnosis

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