Selecting and Utilizing Sequential Residual Generators in FDI Applied to Hybrid Vehicles

Sundström, Christofer; Frisk, Erik; Nielsen, Lars

doi:10.1109/tsmc.2013.2248147

Cited by 22 publications

(12 citation statements)

References 21 publications

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“…whereȳ (q) andū (q) are vectors of y and u and their time derivatives up to order q, respectively, see, e.g., [10,11,30,31]. In case of measurement noise, the residuals (4) are not identically zero in the fault-free case.…”

Section: Analytical Redundancy Relationsmentioning

confidence: 99%

Incident detection and isolation in drilling using analytical redundancy relations

Willersrud

Blanke

Imsland

2015

Control Engineering Practice

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Early diagnosis of incidents that could delay or endanger a drilling operation for oil or gas is essential to limit field development costs. Warnings about downhole incidents should come early enough to allow intervention before it develops to a threat, but this is difficult, since false alarms must be avoided. This paper employs model-based diagnosis using analytical redundancy relations to obtain residuals which are affected differently by the different incidents. Residuals are found to be non-Gaussian -they follow a multivariate t-distribution -hence, a dedicated generalized likelihood ratio test is applied for change detection. Data from a 1400 meter horizontal flow loop test facility is used to assess the diagnosis method. Diagnosis properties of the method are investigated assuming either with available downhole pressure sensors through wired drill pipe or with only topside measurements available. In the latter case, isolation capability is shown to be reduced to group-wise isolation, but the method would still detect all serious events with the prescribed false alarm probability.

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Section: Analytical Redundancy Relationsmentioning

confidence: 99%

Incident detection and isolation in drilling using analytical redundancy relations

Willersrud

Blanke

Imsland

2015

Control Engineering Practice

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“…However, in general the choice may strongly affect the performance of the diagnosis system, which is shown in Section VI. The method used to automatically generate the sequential residual generators is described in [3], that is based on [4], but the key ideas are briefly described below.…”

Section: Automatic Residual Generator Constructionmentioning

confidence: 99%

“…Methods for handling multiple solutions are available [3], but in this work only unique residual generators are considered. Dynamic residual generators often occurs when monitoring physical systems.…”

Section: Automatic Residual Generator Constructionmentioning

confidence: 99%

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Sequential residual generator selection for fault detection

Eriksson

Sundström

2014

2014 European Control Conference (ECC)

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Abstract-Structural methods in model-based fault diagnosis applications are simple and efficient tools for finding candidates for residual generation. However, the structural methods do not take model uncertainties and information about fault behavior into consideration. This may result in selecting residual generators with bad performance to be included in the diagnosis system. By using the Kullback-Leibler divergence, the performance of different residual generators can be compared to find the best one. With the ability to quantify diagnostic performance, the design of residual generators can be optimized by, for example, combining several residual generators such that the diagnostic performance is maximized. The proposed method for residual generation selection is applied to a water tank system to show that the achieved residual performance is improved compared to only use a structural method.

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“…To meet these requirements, the use of Structural Analysis can be advised [20]. Many works available in the literature apply this technique for system diagnosis on, e.g., engines [22,23], hybrid vehicles [24] and other complex systems [25,26,27], but only limited number of papers focus on fuel cells [14,28].…”

Section: Introductionmentioning

confidence: 99%

Model-based diagnosis through Structural Analysis and Causal Computation for automotive Polymer Electrolyte Membrane Fuel Cell systems

Polverino

Frisk

Jung

et al. 2017

Journal of Power Sources

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The present paper proposes an advanced approach for Polymer Electrolyte Membrane Fuel Cell (PEMFC) systems fault detection and isolation through a model-based diagnostic algorithm. The considered algorithm is developed upon a lumped parameter model simulating a whole PEMFC system oriented towards automotive applications. This model is inspired by other models available in the literature, with further attention to stack thermal dynamics and water management. The developed model is analysed by means of Structural Analysis, to identify the correlations among involved physical variables, defined equations and a set of faults which may occur in the system (related to both auxiliary components malfunctions and stack degradation phenomena). Residual generators are designed by means of Causal Computation analysis and the maximum theoretical fault isolability, achievable with a minimal number of installed sensors, is investigated. The achieved results proved the capability of the algorithm to theoretically detect and isolate almost all faults with the only use of stack voltage and temperature sensors, with significant advantages from an industrial point of view. The effective fault isolability is proved through fault simulations at a specific fault magnitude with an advanced residual evaluation technique, to consider quantitative residual deviations from normal conditions and achieve univocal fault isolation

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Selecting and Utilizing Sequential Residual Generators in FDI Applied to Hybrid Vehicles

Cited by 22 publications

References 21 publications

Incident detection and isolation in drilling using analytical redundancy relations

Incident detection and isolation in drilling using analytical redundancy relations

Sequential residual generator selection for fault detection

Model-based diagnosis through Structural Analysis and Causal Computation for automotive Polymer Electrolyte Membrane Fuel Cell systems

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