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A method for quantitative fault diagnosability analysis of stochastic linear descriptor models
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
2013 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 49, no 6, 1591-1600 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2013. Vol. 49, no 6, 1591-1600 p.
Keyword [en]
Fault diagnosability analysis; Fault detection and isolation; Model-based diagnosis
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-89941DOI: 10.1016/j.automatica.2013.02.045ISI: 000319540500007OAI: oai:DiVA.org:liu-89941DiVA: diva2:610501
Available from: 2013-03-11 Created: 2013-03-11 Last updated: 2017-12-06Bibliographically approved
In thesis
1. Diagnosability analysis and FDI system design for uncertain systems
Open this publication in new window or tab >>Diagnosability analysis and FDI system design for uncertain systems
2013 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Our society depends on advanced and complex technical systems and machines, for example, cars for transportation, industrial robots in production lines, satellites for communication, and power plants for energy production. Consequences of a fault in such a system can be severe and result in human casualties, environmentally harmful emissions, high repair costs, or economical losses caused by unexpected stops in production lines. Thus, a diagnosis system is important, and in some applications also required by legislations, to monitor the system health in order to take appropriate preventive actions when a fault occurs. Important properties of diagnosis systems are their capability of detecting and identifying faults, i.e., their fault detectability and isolability performance.

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 diagnosability performance given a mathematical model of the system to be monitored before a diagnosis system is developed. A measure of fault diagnosability performance, called distinguishability, is proposed based on the Kullback-Leibler divergence. For linear descriptor models with Gaussian noise, distinguishability gives an upper limit for the fault to noise ratio of any linear residual generator. Distinguishability is used to analyze fault detectability and isolability performance of a non-linear mean value engine model of gas flows in a heavy duty diesel engine by linearizing the model around different operating points.

It is also shown how distinguishability is used for determine sensor placement, i.e, where sensors should be placed in a system to achieve a required fault diagnosability performance. The sensor placement problem is formulated as an optimization problem, where minimum required diagnosability performance is used as a constraint. Results show that the required diagnosability performance greatly affects which sensors to use, which is not captured if not model uncertainties and measurement noise are taken into consideration.

Another problem considered here is the on-line sequential test selection problem. Distinguishability is used to quantify the performance of the different test quantities. The set of test quantities is changed on-line, depending on the output of the diagnosis system. Instead of using all test quantities the whole time, changing the set of active test quantities can be used to maintain a required diagnosability performance while reducing the computational cost of the diagnosis system. Results show that the number of used test quantities can be greatly reduced while maintaining a good fault isolability performance.

A quantitative diagnosability analysis has been used during the design of an engine misfire detection algorithm based on the estimated torque at the flywheel. Decisions during the development of the misfire detection algorithm are motivated using quantitative analysis of the misfire detectability performance. Related to the misfire detection problem, a flywheel angular velocity model for misfire simulation is presented. An evaluation of the misfire detection algorithm show results of good detection performance as well as low false alarm rate.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2013. 19 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1584
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-89947 (URN)LIU-TEK-LIC-2013:18 (Local ID)978-91-7519-652-7 (ISBN)LIU-TEK-LIC-2013:18 (Archive number)LIU-TEK-LIC-2013:18 (OAI)
Presentation
2013-04-05, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 10:15 (Swedish)
Opponent
Supervisors
Available from: 2013-03-12 Created: 2013-03-12 Last updated: 2013-05-07Bibliographically approved
2. 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. 39 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1660
Keyword
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: 2015-04-23Bibliographically approved

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