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Methods for Automated Design of Fault Detection and Isolation Systems with Automotive Applications
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Fault detection and isolation (FDI) is essential for dependability of complex technical systems. One important application area is automotive systems, where precise and robust FDI is necessary in order to maintain low exhaust emissions, high vehicle up-time, high vehicle safety, and efficent repair. To achieve good performance, and at the same time minimize the need for expensive redundant hardware, model-based FDI is necessary. A model-based FDI-system typically comprises fault detection by means of residual generation and residual evaluation, and finally fault isolation.

The overall objective of this thesis is to develop generic and theoretically sound methods for design of model-based FDI-systems. The developed methods are aimed at supporting an automated design methodology. To this end, the methods require a minimum of human interaction. By means of an automated design methodology the overall design process becomes more efficient and systematic, which also contributes to higher quality. These aspects are of particular importance in an industrial context.

Design of a model-based FDI-system for a complex real-world system is an intricate task that poses several difficulties and challenges that must be handled by the involved design methods. For instance, modeling of these systems often result in large-scale, non-linear, differential-algebraic models. Furthermore, despite substantial modeling work, models are typically not able to capture the behaviors of systems in all operating modes. This results in model-errors of time-varying nature and magnitude. This thesis develops a set of methods able to handle these issues in a systematic manner.

Two methods for model-based residual generation are developed. The two methods handle different stages of the design of residual generators. The first method considers the actual residual generator realization by means of sequential residual generation with mixed causality. The second method considers the problem of how to select an optimal set of residual generators from all possible residual generators that can be created with the first method. Together the two methods enable systematic design of a set of residual generators that fulfills a stated fault isolation requirement. Moreover, the methods are applicable to complex, large-scale, and non-linear differential-algebraic models.

Furthermore, a data-driven method for statistical residual evaluation is developed. The method relies on a comparison of the probability distributions of residuals and exploits no-fault data from the system in order to learn the behavior of no-fault residuals. The method can be used to design residual evaluators capable of handling residuals subject to stochastic uncertainties and disturbances caused by for instance time-varying model errors.

The developed methods, as well as the potential of an automated design methodology, are evaluated through extensive application studies. To verify their generality, the methods are applied to different automotive systems, as well as a wind turbine system. The performances of the obtained FDI-systems are good in relation to the required engineering effort. Particularly, no specific adaption or no tuning of the methods, or the design methodology, were made.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. , 35 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1448
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-77073ISBN: 978-91-7519-894-1 (print)OAI: oai:DiVA.org:liu-77073DiVA: diva2:524827
Public defence
2012-06-15, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2012-05-08 Created: 2012-05-04 Last updated: 2012-05-08Bibliographically approved
List of papers
1. Residual Generators for Fault Diagnosis Using Computation Sequences With Mixed Causality Applied to Automotive Systems
Open this publication in new window or tab >>Residual Generators for Fault Diagnosis Using Computation Sequences With Mixed Causality Applied to Automotive Systems
2010 (English)In: IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS, ISSN 1083-4427, Vol. 40, no 6, 1310-1328 p.Article in journal (Refereed) Published
Abstract [en]

An essential step in the design of a model-based diagnosis system is to find a set of residual generators fulfilling stated fault detection and isolation requirements. To be able to find a good set, it is desirable that the method used for residual generation gives as many candidate residual generators as possible, given a model. This paper presents a novel residual generation method that enables simultaneous use of integral and derivative causality, i.e., mixed causality, and also handles equation sets corresponding to algebraic and differential loops in a systematic manner. The method relies on a formal framework for computing unknown variables according to a computation sequence. In this framework, mixed causality is utilized, and the analytical properties of the equations in the model, as well as the available tools for algebraic equation solving, are taken into account. The proposed method is applied to two models of automotive systems, a Scania diesel engine, and a hydraulic braking system. Significantly more residual generators are found with the proposed method in comparison with methods using solely integral or derivative causality.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA, 2010
Keyword
Fault diagnosis, model-based diagnosis, nonlinear systems, residual generation
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-61313 (URN)10.1109/TSMCA.2010.2049993 (DOI)000283447200015 ()
Note
©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Carl Svärd and Mattias Nyberg, Residual Generators for Fault Diagnosis Using Computation Sequences With Mixed Causality Applied to Automotive Systems, 2010, IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS, (40), 6, 1310-1328. http://dx.doi.org/10.1109/TSMCA.2010.2049993 Available from: 2010-11-12 Created: 2010-11-12 Last updated: 2012-05-08
2. Realizability Constrained Selection of Residual Generators for Fault Diagnosis with an Automotive Engine Application
Open this publication in new window or tab >>Realizability Constrained Selection of Residual Generators for Fault Diagnosis with an Automotive Engine Application
2013 (English)In: IEEE Transactions on Systems, Man and Cybernetics: Systems, ISSN 2168-2216, Vol. 43, no 6, 1354-1369 p.Article in journal (Refereed) Published
Abstract [en]

This paper considers the problem of selecting a set of residual generators for inclusion in a model-based diagnosis system, while fulfilling fault isolability requirements and minimizing the number of residual generators. Two novel algorithms for solving the selection problem are proposed. The first algorithm provides an exact solution fulfilling both requirements and is suitable for small problems. The second algorithm, which constitutes the main contribution, is suitable for large problems and provides an approximate solution by means of a greedy heuristic and by relaxing the minimal cardinality requirement. The foundation for the algorithms is a novel formulation of the selection problem which enables an efficient reduction of the search-space by taking into account realizability properties, with respect to the considered residual generation method. Both algorithms are general in the sense that they are aimed at supporting any computerized residual generation method. In a case study the greedy selection algorithm is successfully applied in an industrial sized automotive engine system.

Place, publisher, year, edition, pages
IEEE, 2013
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-77191 (URN)10.1109/TSMC.2013.2258906 (DOI)000326499800008 ()
Available from: 2012-05-08 Created: 2012-05-08 Last updated: 2013-12-10Bibliographically approved
3. Data-Driven and Adaptive Statistical Residual Evaluation for Fault Detection with an Automotive Application
Open this publication in new window or tab >>Data-Driven and Adaptive Statistical Residual Evaluation for Fault Detection with an Automotive Application
2014 (English)In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 45, no 1, 170-192 p.Article in journal (Refereed) Published
Abstract [en]

An important step in model-based fault detection is residual evaluation, where residuals are evaluated with the aim to detect changes in their behavior caused by faults. To handle residuals subject to time-varying uncertainties and disturbances, which indeed are present in practice, a novel statistical residual evaluation approach is presented. The main contribution is to base the residual evaluation on an explicit comparison of the probability distribution of the residual, estimated online using current data, with a no-fault residual distribution. The no-fault distribution is based on a set of a-priori known no-fault residual distributions, and is continuously adapted to the current situation. As a second contribution, a method is proposed for estimating the required set of no-fault residual distributions off-line from no-fault training data.The proposed residual evaluation approach is evaluated with measurement data on a residual for diagnosis of the gas-flow system of a Scania truck diesel engine. Results show that small faults can be reliable detected with the proposed approach in cases where regular methods fail.

Place, publisher, year, edition, pages
Elsevier, 2014
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-77190 (URN)10.1016/j.ymssp.2013.11.002 (DOI)000331351700012 ()
Available from: 2012-05-08 Created: 2012-05-08 Last updated: 2017-12-07Bibliographically approved
4. Automotive engine FDI by application of an automated model-based and data-driven design methodology
Open this publication in new window or tab >>Automotive engine FDI by application of an automated model-based and data-driven design methodology
2013 (English)In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 21, no 4, 455-472 p.Article in journal (Refereed) Published
Abstract [en]

Fault detection and isolation (FDI) in automotive diesel engines is important in order to achieve and guarantee low exhaust emissions, high vehicle uptime, and efficient repair and maintenance. This paper illustrates how a set of general methods for model-based sequential residual generation and data-driven statistical residual evaluation can be combined into an automated design methodology. The automated design methodology is then utilized to create a complete FDI-system for an automotive diesel engine. The performance of the obtained FDI-system is evaluated using measurements from road drives and engine test-bed experiments. The overall performance of the FDI-system is good in relation to the required design effort. In particular no specific tuning of the FDI-system, or any adaption of the design methodology, was needed. It is illustrated how estimations of the statistical powers of the fault detection tests in the FDI-system can be used to further increase the performance, specifically in terms of fault isolability.

Place, publisher, year, edition, pages
Elsevier, 2013
Keyword
Fault diagnosis, Fault detection, Fault detection and isolation, Automotive diesel engine
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-77189 (URN)10.1016/j.conengprac.2012.12.006 (DOI)000316036500011 ()
Note

Funding Agencies|Scania||VINNOVA (Swedish Governmental Agency for Innovation Systems)||

Available from: 2012-05-08 Created: 2012-05-08 Last updated: 2017-12-07Bibliographically approved
5. Automated Design of an FDI-System for the Wind Turbine Benchmark
Open this publication in new window or tab >>Automated Design of an FDI-System for the Wind Turbine Benchmark
2012 (English)In: Journal of Control Science and Engineering, ISSN 1687-5249, E-ISSN 1687-5257, Vol. 2012, no 989873Article in journal (Refereed) Published
Abstract [en]

We propose an FDI system for the wind turbine benchmark designed by the application of a generic automated method. No specific adaptation of the method for the wind turbine benchmark is needed, and the number of required human decisions, assumptions, as well as parameter choices is minimized. The method contains in essence three steps: generation of candidate residual generators, residual generator selection, and diagnostic test construction. The proposed FDI system performs well in spite of no specific adaptation or tuning to the benchmark. All faults in the predefined test sequence can be detected and all faults, except a double fault, can also be isolated shortly thereafter. In addition, there are no false or missed detections.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-77188 (URN)10.1155/2012/989873 (DOI)
Available from: 2012-05-08 Created: 2012-05-08 Last updated: 2017-12-07Bibliographically approved

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