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Residual Generators for Fault Diagnosis Using Computation Sequences With Mixed Causality Applied to Automotive Systems
Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
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. Vol. 40, no 6, 1310-1328 p.
Keyword [en]
Fault diagnosis, model-based diagnosis, nonlinear systems, residual generation
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-61313DOI: 10.1109/TSMCA.2010.2049993ISI: 000283447200015OAI: oai:DiVA.org:liu-61313DiVA: diva2:369815
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
In thesis
1. Residual Generation Methods for Fault Diagnosis with Automotive Applications
Open this publication in new window or tab >>Residual Generation Methods for Fault Diagnosis with Automotive Applications
2009 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The problem of fault diagnosis consists of detecting and isolating faults present in a system. As technical systems become more and more complex and the demands for safety, reliability and environmental friendliness are rising, fault diagnosis is becoming increasingly important. One example is automotive systems, where fault diagnosis is a necessity for low emissions, high safety, high vehicle uptime, and efficient repair and maintenance.

One approach to fault diagnosis, providing potentially good performance and in which the need for additional hardware is minimal, is model-based fault diagnosis with residuals. A residual is a signal that is zero when the system under diagnosis is fault-free, and non-zero when particular faults are present in the system. Residuals are typically generated by using a mathematical model of the system and measurements from sensors and actuators. This process is referred to as residual generation.

The main contributions in this thesis are two novel methods for residual generation. In both methods, systems described by Differential-Algebraic Equation (DAE) models are considered. Such models appear in a large class of technical systems, for example automotive systems. The first method consider observer-based residual generation for linear DAE-models. This method places no restrictions on the model, such as e.g. observability or regularity, in comparison with other previous methods. If the faults of interest can be detected in the system, the output from the design method is a residual generator, in state-space form, that is sensitive to the faults of interest. The method is iterative and relies on constant matrix operations, such as e.g. null-space calculations and equivalence transformations.

In the second method, non-linear DAE-models are considered. The proposed method belongs to a class of methods, in this thesis referred to as sequential residual generation, which has shown to be successful for real applications. This method enables simultaneous use of integral and derivative causality, and is able to handle equation sets corresponding to algebraic and differential loops in a systematic manner. It relies on a formal framework for computing unknown variables in the model according to a computation sequence, in which the analytical properties of the equations in the model as well as the available tools for equation solving are taken into account. The method is successfully applied to complex models of an automotive diesel engine and a hydraulic braking system.

Place, publisher, year, edition, pages
Linkö: Linköping University Electronic Press, 2009. 28 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1406
Keyword
Diagnosis, fault diagnosis, FDI, fault detection, residual, residual generation, residual generator, DAE
National Category
Information Science
Identifiers
urn:nbn:se:liu:diva-19104 (URN)LIU-TEK-LIC-2009:14 (Local ID)978-91-7393-608-8 (ISBN)LIU-TEK-LIC-2009:14 (Archive number)LIU-TEK-LIC-2009:14 (OAI)
Presentation
2009-06-04, Visionen, B-huset, ingång 27, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2009-06-15 Created: 2009-06-11 Last updated: 2012-05-08Bibliographically approved
2. Methods for Automated Design of Fault Detection and Isolation Systems with Automotive Applications
Open this publication in new window or tab >>Methods for Automated Design of Fault Detection and Isolation Systems with Automotive Applications
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:nbn:se:liu:diva-77073 (URN)978-91-7519-894-1 (ISBN)
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

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