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Diagnosability performance analysis of models and fault detectors
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.ORCID iD: 0000-0003-0808-052X
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 [en]
Fault detection, Fault isolation, FDI, Kullback-Leibler divergence, Engine misfire detection
National Category
Control Engineering Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-117058DOI: 10.3384/diss.diva-117058ISBN: 978-91-7519-080-8 (print)OAI: oai:DiVA.org:liu-117058DiVA: diva2:806708
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
List of papers
1. A method for quantitative fault diagnosability analysis of stochastic linear descriptor models
Open this publication in new window or tab >>A method for quantitative fault diagnosability analysis of stochastic linear descriptor models
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
Keyword
Fault diagnosability analysis; Fault detection and isolation; Model-based diagnosis
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-89941 (URN)10.1016/j.automatica.2013.02.045 (DOI)000319540500007 ()
Available from: 2013-03-11 Created: 2013-03-11 Last updated: 2017-12-06Bibliographically approved
2. Asymptotic behavior of fault diagnosis performance
Open this publication in new window or tab >>Asymptotic behavior of fault diagnosis performance
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Fault detection and fault isolation performance of a model based diagnosis system mainly depends on the level of model uncertainty and the time allowed for detection. The longer time for detection that can be accepted, the more certain detection can be achieved and the main objective of this paper is to show how the window length relates to diagnosis performance. A key result is an explicit expression for asymptotic performance with respect to window length. A key property of the approach is that the model is analyzed directly, which makes the approach independent of detection filter design. It is shown that there exists a linear asymptote as the window length tends to infinity and it is also shown how this linear asymptote can be computed as well as higher order approximations.

National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-117174 (URN)
Available from: 2015-04-21 Created: 2015-04-21 Last updated: 2015-04-21Bibliographically approved
3. Quantitative isolability analysis of different fault modes
Open this publication in new window or tab >>Quantitative isolability analysis of different fault modes
2015 (English)In: 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2015 – Paris, 2–4 September 2015: Proceedings / [ed] Didier Maquin, Elsevier, 2015, Vol. 48(21), 1275-1282 p.Conference paper, Published paper (Refereed)
Abstract [en]

To be able to evaluate quantitative fault diagnosability performance in model-based diagnosis is useful during the design of a diagnosis system. Different fault realizations are more or less likely to occur and the fault diagnosis problem is complicated by model uncertainties and noise. Thus, it is not obvious how to evaluate performance when all of this information is taken into consideration. Four candidates for quantifying fault diagnosability performance between fault modes are discussed. The proposed measure is called expected distinguishability and is based of the previous distinguishability measure and two methods to compute expected distinguishability are presented.

Place, publisher, year, edition, pages
Elsevier, 2015
Series
IFAC-PapersOnLine, ISSN 2405-8963
Keyword
Fault detection and isolation, quantitative diagnosability analysis, Kullback-Leibler divergence
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Computer Engineering
Identifiers
urn:nbn:se:liu:diva-117175 (URN)10.1016/j.ifacol.2015.09.701 (DOI)
Conference
9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2015 – Paris, France, 2–4 September 2015
Note

At the time for thesis presentation publication was in status: Manuscript

Available from: 2015-04-21 Created: 2015-04-21 Last updated: 2016-11-18Bibliographically approved
4. Sensor selection for fault diagnosis in uncertain systems
Open this publication in new window or tab >>Sensor selection for fault diagnosis in uncertain systems
Show others...
(English)Manuscript (preprint) (Other academic)
Abstract [en]

The goal of this work is to find the cheapest set of sensors such that a designed diagnosis system can achieve required fault detectability and isolability performance. Algorithms have been developed that find sets of sensors that makes 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. Here, a quantitative measure of diagnosability performance, called distinguishability, is used to quantify diagnosability performance given a set of sensors. The sensor selection problem is then formulated, using distinguishability, to assure that the set of sensors fulfills required performance specifications also when model uncertainties and measurement noise are taken into consideration. However, the algorithms that can be employed for finding the optimal solution are intractible, and it is demonstrated why it is hard to find optimal solutions to the sensor selection problem without exhaustive search. Therefore, the use of a greedy stochastic search algorithm is proposed to solve the sensor selection problem. A case study is used to show how the greedy stochastic search is able to find sets of sensors close to the global optimum in short computational time.

National Category
Electrical Engineering, Electronic Engineering, Information Engineering Computer Engineering
Identifiers
urn:nbn:se:liu:diva-117176 (URN)
Available from: 2015-04-21 Created: 2015-04-21 Last updated: 2015-04-21Bibliographically approved
5. Development of misfire detection algorithm using quantitative FDI performance analysis
Open this publication in new window or tab >>Development of misfire detection algorithm using quantitative FDI performance analysis
2015 (English)In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 34, 49-60 p.Article in journal (Refereed) Published
Abstract [en]

A model-based misfire detection algorithm is proposed. The algorithm is able to detect misfires and identify the failing cylinder during different conditions, such as cylinder-to-cylinder variations, cold starts, and different engine behavior in different operating points. Also, a method is proposed for automatic tuning of the algorithm based on training data. The misfire detection algorithm is evaluated using data from several vehicles on the road and the results show that a low misclassification rate is achieved even during difficult conditions. (C) 2014 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
Elsevier, 2015
Keyword
Misfire detection; Fault diagnosis; Fault detection and isolation; Kullback-Leibler divergence; Pattern recognition
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-114011 (URN)10.1016/j.conengprac.2014.10.001 (DOI)000347599500005 ()
Note

Funding Agencies|Volvo Car Corporation; Swedish Research Council within the Linnaeus Center CADICS

Available from: 2015-02-06 Created: 2015-02-05 Last updated: 2017-12-04
6. A flywheel manufacturing error compensation algorithm for engine misfire detection
Open this publication in new window or tab >>A flywheel manufacturing error compensation algorithm for engine misfire detection
2016 (English)In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 47, 37-47 p.Article in journal (Refereed) Published
Abstract [en]

A commonly used signal for engine misfire detection is the crankshaft angular velocity measured at the flywheel. However, flywheel manufacturing errors result in vehicle-to-vehicle variations in the measurements and have a negative impact on the misfire detection performance, where the negative impact is quantified for a number of vehicles. A misfire detection algorithm is proposed with flywheel error adaptation in order to increase robustness and reduce the number of mis-classifications. Since the available computational power is limited in a vehicle, a filter with low computational load, a Constant Gain Extended Kalman Filter, is proposed to estimate the flywheel errors. Evaluations using measurements from vehicles on the road show that the number of mis-classifications is significantly reduced when taking the estimated flywheel errors into consideration.

National Category
Electrical Engineering, Electronic Engineering, Information Engineering Computer Engineering
Identifiers
urn:nbn:se:liu:diva-117177 (URN)10.1016/j.conengprac.2015.12.009 (DOI)000370091900004 ()
Note

Funding agencies:The work is partially supported by the Swedish Research Council within the Linnaeus Center CADICS.

Vid tiden för disputation förelåg publikationen endast som manuskript

Available from: 2015-04-21 Created: 2015-04-21 Last updated: 2017-12-04Bibliographically approved

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Output format
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