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Sensor selection for fault diagnosis in uncertain systems
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
Institute for Software Integrated Systems, Vanderbilt University, Nashville, USA.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Computer Engineering. Linköping University, The Institute of Technology.
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2018 (English)In: International Journal of Control, ISSN 0020-7179, E-ISSN 1366-5820, p. 1-11Article in journal (Refereed) Epub ahead of print
Abstract [en]

Finding the cheapest, or smallest, set of sensors such that a specified level of diagnosis performance is maintained is important to decrease cost while controlling performance. Algorithms have been developed to find sets of sensors that make faults detectable and isolable under ideal circumstances. However, due to model uncertainties and measurement noise, different sets of sensors result in different achievable diagnosability performance in practice. In this paper, the sensor selection problem is formulated to ensure that the set of sensors fulfils required performance specifications when model uncertainties and measurement noise are taken into consideration. However, the algorithms for finding the guaranteed global optimal solution are intractable without exhaustive search. To overcome this problem, a greedy stochastic search algorithm is proposed to solve the sensor selection problem. A case study demonstrates the effectiveness of the greedy stochastic search in finding sets close to the global optimum in short computational time.

Place, publisher, year, edition, pages
Taylor & Francis, 2018. p. 1-11
Keywords [en]
Fault diagnosis, fault detection and isolation, sensor selection
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Computer Engineering
Identifiers
URN: urn:nbn:se:liu:diva-117176DOI: 10.1080/00207179.2018.1484171OAI: oai:DiVA.org:liu-117176DiVA, id: diva2:806672
Note

The previous status of this article was Manuscript.

Available from: 2015-04-21 Created: 2015-04-21 Last updated: 2018-07-14Bibliographically approved
In thesis
1. Diagnosability performance analysis of models and fault detectors
Open this publication in new window or tab >>Diagnosability performance analysis of models and fault detectors
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Model-based diagnosis compares observations from a system with predictions using a mathematical model to detect and isolate faulty components. Analyzing which faults that can be detected and isolated given the model gives useful information when designing a diagnosis system. This information can be used, for example, to determine which residual generators can be generated or to select a sufficient set of sensors that can be used to detect and isolate the faults. With more information about the system taken into consideration during such an analysis, more accurate estimations can be computed of how good fault detectability and isolability that can be achieved.

Model uncertainties and measurement noise are the main reasons for reduced fault detection and isolation performance and can make it difficult to design a diagnosis system that fulfills given performance requirements. By taking information about different uncertainties into consideration early in the development process of a diagnosis system, it is possible to predict how good performance can be achieved by a diagnosis system and avoid bad design choices. This thesis deals with quantitative analysis of fault detectability and isolability performance when taking model uncertainties and measurement noise into consideration. The goal is to analyze fault detectability and isolability performance given a mathematical model of the monitored system before a diagnosis system is developed.

A quantitative measure of fault detectability and isolability performance for a given model, called distinguishability, is proposed based on the Kullback-Leibler divergence. The distinguishability measure answers questions like "How difficult is it to isolate a fault fi from another fault fj?. Different properties of the distinguishability measure are analyzed. It is shown for example, that for linear descriptor models with Gaussian noise, distinguishability gives an upper limit for the fault to noise ratio of any linear residual generator. The proposed measure is used for quantitative analysis of a nonlinear mean value model of gas flows in a heavy-duty diesel engine to analyze how fault diagnosability performance varies for different operating points. It is also used to formulate the sensor selection problem, i.e., to find a cheapest set of available sensors that should be used in a system to achieve required fault diagnosability performance.

As a case study, quantitative fault diagnosability analysis is used during the design of an engine misfire detection algorithm based on the crankshaft angular velocity measured at the flywheel. Decisions during the development of the misfire detection algorithm are motivated using quantitative analysis of the misfire detectability performance showing, for example, varying detection performance at different operating points and for different cylinders to identify when it is more difficult to detect misfires.

This thesis presents a framework for quantitative fault detectability and isolability analysis that is a useful tool during the design of a diagnosis system. The different applications show examples of how quantitate analysis can be applied during a design process either as feedback to an engineer or when formulating different design steps as optimization problems to assure that required performance can be achieved.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2015. p. 39
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1660
Keywords
Fault detection, Fault isolation, FDI, Kullback-Leibler divergence, Engine misfire detection
National Category
Control Engineering Signal Processing
Identifiers
urn:nbn:se:liu:diva-117058 (URN)10.3384/diss.diva-117058 (DOI)978-91-7519-080-8 (ISBN)
Public defence
2015-05-22, Visionen, Hus B, Campus Valla, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2015-04-21 Created: 2015-04-14 Last updated: 2018-07-14Bibliographically approved

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Jung, DanielFrisk, ErikKrysander, Mattias

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