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  • 1.
    Armengol Llobet, J.
    et al.
    n/a.
    Bregon, A.
    n/a.
    Escobet, E
    n/a.
    Gelso, R.
    n/a.
    Krysander, Mattias
    n/a.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Olive, X.
    n/a.
    Pulido, B.
    n/a.
    Trave-Massuyes, L.
    n/a.
    Minimal Structurally Overdetermined Sets for Residual Generation: A Comparison of Alternative Approaches2009In: Proceedings of IFAC Safeprocess'09, Barcelona, Spain, 2009, p. 1480-1485Conference paper (Refereed)
    Abstract [en]

    The issue of residual generation using structural analysis has been studied by several authors. Structural analysis does not permit to generate the analytical expressions of residuals since the model of the system is abstracted by its structure. However, it determines the set of constraints from which residuals can be generated and it provides the computation sequence to be used. This paper presents and compares four recently proposed algorithms that solve this problem.

  • 2.
    Biteus, Jonas
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Frisk, Erik
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Nyberg, Mattias
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Condensed Representation of Global Diagnoses with Minimal Cardinality in Local Diagnoses2006In: 17th International Workshop on Principles of Diagnosis DX-06,2006, 2006Conference paper (Refereed)
  • 3.
    Biteus, Jonas
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Frisk, Erik
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Nyberg, Mattias
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Distributed Diagnosis by Using a Condensed Local Representation of the Global Diagnoses with Minimal Cardinality2006In: 17 International Workshop on Principles of Diagnosis DX-06,2006, Spain: Spain , 2006Conference paper (Refereed)
  • 4.
    Biteus, Jonas
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Nyberg, Mattias
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Frisk, Erik
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    An algorithm for computing the diagnoses with minimal cardinality in a distributed system2008In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 21, no 2, p. 269-276Article in journal (Refereed)
    Abstract [en]

    In fault diagnosis, the set of minimal diagnoses is commonly calculated. However, due to for example limited computation resources, the search for the set of minimal diagnoses is in some applications focused on to the smaller set of diagnoses with minimal cardinality. The key contribution in this paper is an algorithm that calculates the diagnoses with minimal cardinality in a distributed system. The algorithm is constructed such that the computationally intensive tasks are distributed to the different units in the distributed system, and thereby reduces the need for a powerful central diagnostic unit. © 2007 Elsevier Ltd. All rights reserved.

  • 5.
    Biteus, Jonas
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Nyberg, Mattias
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Frisk, Erik
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Åslund, Jan
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Determining a Component's Fault Status and the Status' Readiness2006In: IFAC Safeprocess06,2006, China: IFAC , 2006Conference paper (Refereed)
  • 6.
    Biteus, Jonas
    et al.
    Power-Train Division, Scania.
    Nyberg, Mattias
    Power-Train Division, Scania.
    Frisk, Erik
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Åslund, Jan
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Determining the Fault Status of a Component and its Readiness, with a Distributed Automotive Application2009In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 22, no 3, p. 363-373Article in journal (Refereed)
    Abstract [en]

    In systems using only single-component tests, the fault status of a component is ready if a test only supervising the component has been evaluated. However, if plausibility tests that supervise multiple components are used, then a component can be ready before all tests supervising the component have been evaluated. Based on test results, this paper contributes with conditions on when a component is ready. The conditions on readiness are given for both centralized and distributed systems and are here applied to the distributed diagnostic system in an automotive vehicle.

  • 7.
    Biteus, Jonas
    et al.
    Linköping University, Department of Electrical Engineering.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering.
    Jensen, Mathias
    Power-train Sc.
    Decentralized Diagnosis in Heavy Duty Vehicles2004In: CCSSE,2004, 2004Conference paper (Refereed)
  • 8.
    Biteus, Jonas
    et al.
    Linköping University, Department of Electrical Engineering.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering.
    Jensen, Mathias
    Power-train Scania.
    Distributed Diagnosis for Embedded Systems in Automotive Vehicles2005In: IFAC World Congress,2005, Netherlands: Elsevier , 2005Conference paper (Refereed)
  • 9.
    Buffoni-Rogovchenko, Lena
    et al.
    Linköping University, Department of Computer and Information Science, PELAB - Programming Environment Laboratory. Linköping University, The Institute of Technology.
    Fritzson, Peter
    Linköping University, Department of Computer and Information Science, PELAB - Programming Environment Laboratory. Linköping University, The Institute of Technology.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Garro, Alfredo
    University of Calabria, Italy.
    Tundis, Andrea
    University of Calabria, Italy.
    Requirement Verification and Dependency Tracing During Simulation in Modelica2013In: EUROSIM '13, IEEE Press, 2013, p. 561-566Conference paper (Refereed)
    Abstract [en]

    Requirement verification is an important part of the development process, and the increasing system complexity has exacerbated the need for integrating this step into a formalized model driven development process, providing a dedicated methodology as well as tool support. In this paper the authors propose an extension for Modelica, an equation-based language for system modeling, that will allow to represent system requirements in the same formalism as the design model, thus reducing the need for transformations between different specialized formalisms, lowering maintenance and modification costs, and benefitting from the expression and simulation capabilities, as well as extensive tool support of Modelica. The object-oriented nature of the approach provides the advantages of modular design and hierarchical structuring of the requirement model. This paper also illustrates, with the help of an example, how requirement verification can be used alongside the simulation process to trace the components responsible for requirement violations. To this end, we introduce a formalism for expressing relationships between components and requirements, as well as a tracing algorithm.

  • 10.
    Frisk, Erik
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Krysander, Mattias
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Nyberg, Mattias
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Åslund, Jan
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    A toolbox for design of diagnosis systems2006In: IFAC Safeprocess06,2006, Beijing, China: IFAC , 2006, p. 703-Conference paper (Refereed)
    Abstract [en]

    Design of diagnosis systems is a complex task that involves many different steps. Full understanding of all different parts of the design procedure requires deep knowledge on theory from a wide variety of subjects. Thus, to encourage the use of results from diagnosis research it is highly desirable to have software support in the design process. This paper describes ongoing work for determining an architecture for such a toolbox. The paper also describes software solutions in the toolbox. In industry as well as in universities, Matlab is probably the most widespread tool used by control engineers. Therefore the toolbox is primarily based upon Matlab but also some computer algebraic tools such as Mathematica and Maple are used.

  • 11.
    Frisk, Erik
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Nyberg, Mattias
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering.
    A minimal polynomial basis solution to residual generation for fault diagnosis in linear systems2001In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 37, no 9, p. 1417-1424Article in journal (Refereed)
    Abstract [en]

    A fundamental part of a fault diagnosis system is the residual generator. Here a new method, the minimal polynomial basis approach, for design of residual generators for linear systems, is presented. The residual generation problem is transformed into a problem of finding polynomial bases for null-spaces of polynomial matrices. This is a standard problem in established linear systems theory, which means that numerically efficient computational tools are generally available. It is shown that the minimal polynomial basis approach can find all possible residual generators and explicitly those of minimal order. © 2001 Elsevier Science Ltd. All rights reserved.

  • 12.
    Frisk, Erik
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Nyberg, Mattias
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Residual Generation for Fault Diagnosis of Systems Described by General Linear Differential-algebraic Equations2002In: IFAC World Congress,2002, 2002Conference paper (Refereed)
  • 13.
    Frisk, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Residual Generation for Fault Diagnosis of Systems Described by General Linear Differential-Algebraic Equations (revised)2005Report (Other academic)
    Abstract [en]

    Linear residual generation for DAE systems has been considered. In all results derived, no distinction between input and output signals is done. A complete characterization and parameterization of all residual generators is presented. Further, a condition for fault detectability in DAE systems is given. Based on the characterization of all residual generators, a design strategy for residual generators for DAE systems is presented. Given that a set of faults are detectable, the design strategy will result in a residual generator sensitive to all the detectable faults. Further the residual generator is guaranteed to be of lowest possible order. Special care has been devoted to assure this property also for non-controllable systems.

  • 14.
    Frisk, Erik
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Nyberg, Mattias
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Using Minimal Polynomial Bases for Fault Diagnosis1999In: European Control Conference,1999, 1999Conference paper (Refereed)
  • 15.
    Frisk, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
    Using Minimal Polynomial Bases for Model-Based Fault Diagnosis: A Demonstration Document for PolyX, Ltd1999Report (Other academic)
    Abstract [en]

    This document is a demonstration document, demonstrating the use of the Polynomial Toolbox for Matlab when designing residual generators for fault diagnosis. A brief introduction to the residual generation problem for fault diagnosis in linear systems is given and a solution based on polynomial methods are outlined. Also, a design example, complete with \sc Matlab code illustrates how the Polynomial Toolbox can be used in the design of residual generators.

  • 16.
    Frisk, Erik
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Nyberg, Mattias
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Nielsen, Lars
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    FDI with adaptive residual generation applied to a DC-servo1997In: IFAC Safeprocess,1997, Hull: IFAC , 1997Conference paper (Refereed)
  • 17.
    Kinnaert, M
    et al.
    Linkoping Univ, Dept Elect Engn, SE-58183 Linkoping, Sweden IRISA CNRS, F-35042 Rennes, France.
    Nyberg, Mattias
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering.
    Basseville, M
    Linkoping Univ, Dept Elect Engn, SE-58183 Linkoping, Sweden IRISA CNRS, F-35042 Rennes, France.
    Discussion on: 'On fault detectability and isolability' by M. Basseville2001In: European Journal of Control, ISSN 0947-3580, E-ISSN 1435-5671, Vol. 7, no 6, p. 638-641Other (Other academic)
  • 18.
    Krysander, Mattias
    et al.
    Linköping University, Department of Electrical Engineering.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering.
    Fault Isolability Prediction of Diagnostic Models2005In: 16th International Workshop on Principles of Diagnosis DX-05,2005, Pacific Grove, California, USA, 2005, p. 163-168Conference paper (Refereed)
    Abstract [en]

    Fault isolability plays a significant role and could be critical with respect to many aspects such as safety and maintenance for a process to be diagnosed. In the development of processes including diagnosis, design decisions are taken, e.g. sensor configuration selection, which affects the fault isolability possibilities. In this paper an algorithm for predicting fault isolability possibilities using a structural model describing the process is proposed. Since only a structural model is needed as input, the algorithm can easily predict fault isolability possibilities of different design concepts. In contrast to previous algorithms using structural models no assumption is imposed on the model. The algorithm computes faults that cannot be distinguished from other faults, which can be used to exclude design alternatives with insufficient isolability possibility.

  • 19.
    Krysander, Mattias
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering.
    Nyberg, Mattias
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering.
    Statistical Properties and Design Criterions for Fault Isolation in Noisy Systems2008In: 19th International Workshop on Principles of Diagnosis, DX,2008, Sydney, Australia: DX , 2008Conference paper (Refereed)
    Abstract [en]

    Fault diagnosis in the presence of noise and model errors is of fundamental importance. In the paper, the meaning of fault isolation performance is formalized by using the established notion of coverage and false coverage from the field of statistics. Then formal relations describing the relationship between fault isolation performance and the residual related design parameters are derived. For small faults, the measures coverage and false coverage are not applicable so therefore, a different performance criteria, called sub-coverage, is proposed. The performance of different AI-based fault isolation schemes is evaluated and it is notably shown that the well known principle of minimal cardinality diagnosis gives a bad performance. Finally, some general design guidelines that guarantee and maximize the fault isolation performance are proposed.

  • 20.
    Krysander, Mattias
    et al.
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Structural Analysis for Fault Diagnosis of DAE Systems Utilizing Graph Theory and MSS Sets2002Report (Other academic)
    Abstract [en]

    When designing model-based fault-diagnostic systems, the use of consistency relations (also called e.g. parity relations) is a common choice. Different consistency relations are sensitive to different subsets of faults, and thereby isolation can be achieved. This report presents an algorithm for finding a small set of submodels that can be used to derive consistency relations with highest possible diagnosis capability. The algorithm handles differential-algebraic models and is based on graph theoretical reasoning about the structure of the model. An important step towards finding these submodels, and therefore also towards finding consistency relations, is to find all minimal structurally singular (MSS) sets of equations. These sets characterize the fault diagnosability. The algorithm is applied to a large nonlinear industrial example, a part of a paper plant. In spite of the complexity of this process, a small set of consistency relations with high diagnosis capability is successfully derived.

  • 21.
    Krysander, Mattias
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Åslund, Jan
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Nyberg, Mattias
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering.
    An Efficient Algorithm for Finding Minimal Overconstrained Subsystems for Model-Based Diagnosis2008In: IEEE transactions on systems, man and cybernetics. Part A. Systems and humans, ISSN 1083-4427, E-ISSN 1558-2426, Vol. 38, no 1, p. 197-206Article in journal (Refereed)
    Abstract [en]

    In model based diagnosis, the diagnostic system construction is based on a model of the technical system to be diagnosed. To handle large differential algebraic models and to achieve fault isolation, a common strategy is to pick out small over-constrained parts of the model and to test these separately against measured signals. A new algorithm for computing all minimal over-constrained sub-systems in a model is proposed. For complexity comparison, previous algorithms are recalled. It is shown that the time complexity under certain conditions is much better for the new algorithm. This is illustrated using a truck engine model.

  • 22.
    Krysander, Mattias
    et al.
    Linköping University, Department of Electrical Engineering.
    Åslund, Jan
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering.
    An Efficient Algorithm for Finding Over-constrained Sub-systems for Construction of Diagnostic Tests2005In: 16th International Workshop on Principles of Diagnosis DX-05,2005, Pacific Grove, California, USA, 2005, p. 55-60Conference paper (Refereed)
    Abstract [en]

    In this paper, a new algorithm for computing all minimal over-constrained sub-systems in a structural model is proposed. To handle large differential algebraic models in diagnosis, systematic structural approaches to find testable sub-systems have been suggested. It is shown how the algorithm can be incorporated and improve some of them. Previous algorithms are recalled and it is shown that the new algorithm is 14000 times faster when applied to a Scania truck engine model.

  • 23.
    Nielsen, Lars
    et al.
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
    Frisk, Erik
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
    Bäckström, Christer
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
    Henriksson, Anders
    Linköping University, Department of Computer and Information Science. Linköping University, Faculty of Science & Engineering.
    Klein, Inger
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Gunnarsson, Svante
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Issues in Diagnosis, Supervision, and Safety1996Report (Other academic)
    Abstract [en]

    Issues concerning diagnosis, supervision and saftey are found in many technologically advanced products. There is now a trend to extend the functionality of diagnosis and supervision systems to handle more advanced situations. This report collects some of the initiatives taking place in research and some of the developments taking place in the industry.

  • 24.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    A Generalized Minimal Hitting-Set Algorithm to Handle Diagnosis With Behavioral Modes2011In: IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS, ISSN 1083-4427, Vol. 41, no 1, p. 137-148Article in journal (Refereed)
    Abstract [en]

    To handle diagnosis with behavioral modes, a new generalized minimal hitting-set algorithm is presented. The key properties in comparison with that of the original minimal hitting-set algorithm given by de Kleer and Williams are that it can handle more than two modes per component and also nonpositive conflicts. The algorithm computes a logical formula that characterizes all diagnoses. Instead of minimal or kernel diagnoses, some specific conjunctions in the logical formula are used to characterize the diagnoses. These conjunctions are a generalization of both minimal and kernel diagnoses. From the logical formulas, it is also easy to derive the set of preferred diagnoses. One usage of the algorithm is fault isolation in the sense of fault detection and isolation (FDI). The algorithm is experimentally shown to provide significantly better performance compared to the fault isolation approach based on structured residuals, which is commonly used in FDI.

  • 25.
    Nyberg, Mattias
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering.
    Criterions for detectability and strong detectability of faults in linear systems2002In: International Journal of Control, ISSN 0020-7179, E-ISSN 1366-5820, Vol. 75, no 7, p. 490-501Article in journal (Refereed)
    Abstract [en]

    A fault is (strongly) detectable if it is possible to construct a residual generator that is sensitive to the (constant) fault while decoupling all disturbances. Existing fault detectability criterions are reviewed and in two cases, improved versions are derived. For strong fault detectability, three new criterions are presented. To prove all criterions, a framework of polynomial bases is utilized. With these new and improved criterions, there exists now a criterion for models given both on transfer function form and state-space form, and for both fault detectability and strong fault detectability investigations. Recommendations are given on what criterion to use in different situations.

  • 26.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Design of a Complete FDI System based on a Performance Index With Application to an Automotive Engine1997Report (Other academic)
    Abstract [en]

    Assuming residual generators are already available, there are still several choices to be made when a complete FDI system is to be designed. This is a time-consuming engineering work so for this purpose, a systematic procedure is proposed. The procedure is phrased as an optimization problem. The goal is to minimize a new probability based performance index, which is derived from measurements on the real process. To increase the robustness of the FDI system, a don’t care option is introduced in the residual structure. The procedure is successfully applied to the problem of FDI design for the air intake system of an SI-engine.

  • 27.
    Nyberg, Mattias
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering.
    Model-based diagnosis of an automotive engine using several types of fault models2002In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 10, no 5, p. 679-689Article in journal (Refereed)
    Abstract [en]

    Automotive engines is an important application for model-based diagnosis because of legislative regulations. A diagnosis system for the air-intake system of a turbo-charged engine is constructed. The design is made in a systematic way and follows a framework of hypothesis testing. Different types of sensor faults and leakages are considered. It is shown how many different types of fault models, e.g., additive and multiplicative faults, can be used within one common diagnosis system, and using the same underlying design principle. The diagnosis system is experimentally validated on a real engine using industry-standard dynamic test-cycles.

  • 28.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
    The Polybox Example using the Framework of Structured Hypothesis Tests2001Report (Other academic)
    Abstract [en]

    The POLYBOX example is a standard example within the AI-field of model-based diagnosis research. Here, this example is discussed in the perspective of structured hypothesis tests (SHT). Even though the SHT framework was primarily developed for handling systems with noise, it has here been shown that it can perform very well in also noise-free systems. In the POLYBOX example, it manage to always give a complete and logically sound diagnosis statement, i.e. a complete and correct list of the possible fault modes. On the contrary, the established FDI framework (i.e. structured residuals) only manage to give a subset of the possible fault modes.

  • 29.
    Nyberg, Mattias
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering.
    Using hypothesis testing theory to evaluate principles for leakage diagnosis of automotive engines2003In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 11, no 11, p. 1263-1272Article in journal (Refereed)
    Abstract [en]

    Two different methods for diagnosing leakages in the air path of an automotive engine are investigated. The first is based on a comparison between measured and estimated air flows. The second is based on an estimation of the leakage area. The two methods are compared by using a framework of hypothesis testing and especially the power function. The investigation is made first in theory and then also on a real engine. The conclusion is that the principle based on the estimated leakage area, gives a better power function and is therefore the best choice if only leakage detection is considered. However, if also other faults need to be diagnosed, it is shown that the sensitivity to these other faults may be better with the principle based on comparison of estimated and measured air flow. © 2003 Elsevier Ltd. All rights reserved.

  • 30.
    Nyberg, Mattias
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Frisk, Erik
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    A Minimal Polynomial Basis Solution to Residual Generation for Fault Diagnosis in Linear Systems1999In: IFAC World Congress,1999, 1999Conference paper (Refereed)
  • 31.
    Nyberg, Mattias
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Frisk, Erik
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems.
    Residual generation for fault diagnosis of systems described by linear differential-algebraic equations2006In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 51, no 12, p. 1995-2000Article in journal (Refereed)
    Abstract [en]

    Linear residual generation for differential-algebraic equation (DAE) systems is considered within a polynomial framework where a complete characterization and parameterization of all residual generators is presented. Further, a condition for fault detectability in DAE systems is given. Based on the characterization of all residual generators, a design strategy for residual generators for DAE systems is presented. The design strategy guarantees that the resulting residual generator is sensitive to all the detectable faults and also that the residual generator is of lowest possible order. In all results derived, no assumption about observability or controllability is needed. In particular, special care has been devoted to assure the lowest-order property also for non-controllable systems. © 2006 IEEE.

  • 32.
    Nyberg, Mattias
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering.
    Krysander, Mattias
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering.
    Statistical Properties and Design Criterions for AI-Based Fault Isolation2008In: IFAC World Congress,2008, Seoul, Korea: IFAC , 2008Conference paper (Refereed)
    Abstract [en]

    Fault diagnosis in the presence of noise and model errors is of fundamental importance. In the paper, the meaning of fault isolation performance is formalized by using the established notion of coverage and false coverage from the field of statistics. Then formal relations describing the relationship between fault isolation performance and the residual related design parameters are derived. For small faults, the measures coverage and false coverage are not applicable so therefore, a different performance criteria, called sub-coverage, is proposed. The performance of different AI-based fault isolation schemes is evaluated and it is shown that the well known principle of minimal cardinality diagnosis gives a very bad performance. Finally, some general design guidelines that guarantee and maximize the fault isolation performance are proposed.

  • 33.
    Nyberg, Mattias
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering.
    Nielsen, Lars
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering.
    A universal Chow-Willsky scheme and detectability criteria2000In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 45, no 1, p. 152-156Article in journal (Refereed)
    Abstract [en]

    An important issue in diagnosis research is design methods for residual generation. One method is the Chow-Willsky scheme. Here, the Chow-Willsky scheme is extended as it becomes universal in the sense that, for both discrete and continuous linear systems, it Is shown to be able to generate all possible parity functions. This result means it can also be used to design all possible residual generators. It is shown previous extensions to the Chow-Willsky scheme are not universal, which is the case when dynamics controllable from fault exist, but not from the inputs or disturbances. Also included here are two new conditions on the process for fault detectability and strong fault detectability. A general condition for strong fault detectability has not been presented elsewhere.

  • 34.
    Nyberg, Mattias
    et al.
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Nielsen, Lars
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
    Parity Functions as Universal Residual Generators and Tool for Fault Detectability Analysis1997Conference paper (Refereed)
    Abstract [en]

    The Chow-Willsky scheme is a design method for residual generation. Here an extension to the Chow-Willsky scheme, called the ULPE scheme, is presented. The ULPE scheme is shown to be able to generate all possible residual generators for both discrete and continuous linear systems. It is also shown that previous extensions to the Chow-Willsky scheme do not have this capability. Two new straightforward conditions on the process for fault detectability and strong fault detectability are presented. A general condition for strong fault detectability has not been presented elsewhere. It is shown that fault detectability and strong fault detectability can be seen as system properties rather than properties of the residual generator.

  • 35.
    Pernestål, Anna
    et al.
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Bayesian Fault Diagnosis for Automitive Engines by Combining Data and Process Knowledge2009In: IEEE Transactions on Systems, Man and Cybernetics, ISSN 0018-9472, E-ISSN 2168-2909Article in journal (Other academic)
    Abstract [en]

    We consider fault diagnosis of complex systems, motivated by the problem of fault diagnosis of an automotive diesel engine. Previous fault diagnosis algorithms are typically based either on process knowledge, for example a Fault Signature Matrix (FSM), or on training data. Both these methods have their advantages and drawbacks.

    The main contribution in the present work is that we show how to integrate process knowledge and training data to improve fault diagnosis for automotive processes. We carefully investigate the characteristics of our motivating application, and we derive a new method for fault diagnosis based Bayesian inference.

    To illustrate the new fault diagnosis method we have applied it to the diagnosis of the gas flow of an automotive engine using data from real driving situations. It is shown that diagnosis performance is improved compared to previous methods using solely data or process knowledge. Finally we study the relation between the new method and previous state of the art methods for fault diagnosis.

  • 36.
    Pernestål, Anna
    et al.
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Bayesian Inference by Combining Training Data and Background Knowledge Expressed as Likelihood Constraints2009In: International Journal of Approximate Reasoning, ISSN 0888-613X, E-ISSN 1873-4731Article in journal (Other academic)
    Abstract [en]

    Bayesian inference, or classification, from data is a powerful method for determining states of process when no detailed physical model of the process exists. However, the performance of Bayesian inference from data is dependent on the amount of training data available. In many real applications the amount of training data is limited, and inference results become insufficient. Thus it is important to take other kinds of information into account in the inference as well. In this paper, we consider a general type of background knowledge that appears in many real applications, for example medical diagnosis, technical diagnosis, and econometrics. We show how it can be expressed as constraints on the likelihoods, and provide detailed description of the computations. The method is applied to a diagnosis example, where it is clearly shown how the integration of background knowledge improves diagnosis when training data is limited.

  • 37.
    Pernestål, Anna
    et al.
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Non-stationary Dynamic Bayesian Networks in Modeling of Troubleshooting Process2009In: International Journal of Approximate Reasoning, ISSN 0888-613X, E-ISSN 1873-4731Article in journal (Other academic)
    Abstract [en]

    In research and industry, decision theoretic troubleshooting of complex automotive systems has recently gained increased interest. With suitable troubleshooting, uptime can be increased and repair times shortened. To perform decision theoretic troubleshooting, probability computations are needed. In this work we consider computation of these probabilities under external interventions, which changes dependency relations. We apply a non-stationary dynamic Bayesian network (nsDBN), where the interventions so called events. The events change dependency relations, and drive the nsDBN forward. In the paper, we present how to build models using event driven nsDBN, how to perform inference, and how to use the method in troubleshooting. Event driven nsDBN can be used to model any process subject to interventions, and in particular it opens for solving more general troubleshooting problems than previously presented in literature.

  • 38.
    Pernestål, Anna
    et al.
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Warnquist, Håkan
    Linköping University, Department of Computer and Information Science. Linköping University, The Institute of Technology.
    Modeling and Efficient Inference for Troubleshooting Automotive Systems2009Report (Other academic)
    Abstract [en]

    We consider computer assisted troubleshooting of automotive vehicles, where the objective is to repair the vehicle at as low expected cost as possible.

    The work has three main contributions: a troubleshooting method that applies to troubleshooting in real environments, the discussion on practical issues in modeling for troubleshooting, and the efficient probability computations.

    The work is based on a case study of an auxiliary braking system of a modern truck.

    We apply a decision theoretic approach, consisting of a planner and a diagnoser.

    Two main challenges in troubleshooting automotive vehicles are the need for disassembling the vehicle during troubleshooting to access parts to repair, and the difficulty to verify that the vehicle is fault free. These facts lead to that probabilities for faults and for future observations must be computed for a system that has been subject to external interventions that cause changes the dependency structure. The probability computations are further complicated due to the mixture of instantaneous and non-instantaneous dependencies.

    To compute the probabilities, we develop a method based on an algorithm, updateBN, that updates a static BN to account for the external interventions.

  • 39.
    Pernestål, Anna
    et al.
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Warnquist, Håkan
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Modeling and inference for troubleshooting with interventions applied to a heavy truck auxiliary braking system2012In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 25, no 4, p. 705-719Article in journal (Refereed)
    Abstract [en]

    Computer assisted troubleshooting with external interventions is considered. The work is motivated by the task of repairing an automotive vehicle at lowest possible expected cost. The main contribution is a decision theoretic troubleshooting system that is developed to handle external interventions. In particular, practical issues in modeling for troubleshooting are discussed, the troubleshooting system is described, and a method for the efficient probability computations is developed. The troubleshooting systems consists of two parts; a planner that relies on AO* search and a diagnoser that utilizes Bayesian networks (BN). The work is based on a case study of an auxiliary braking system of a modern truck. Two main challenges in troubleshooting automotive vehicles are the need for disassembling the vehicle during troubleshooting to access parts to repair, and the difficulty to verify that the vehicle is fault free. These facts lead to that probabilities for faults and for future observations must be computed for a system that has been subject to external interventions that cause changes in the dependency structure. The probability computations are further complicated due to the mixture of instantaneous and non-instantaneous dependencies. To compute the probabilities, we develop a method based on an algorithm, updateBN, that updates a static BN to account for the external interventions.

  • 40.
    Pernestål, Anna
    et al.
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    Warnquist, Håkan
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    Modeling and Troubleshooting with Interventions Applied to an Auxiliary Truck Braking System2009In: Proceedings of the 2nd IFAC Workshop on Dependable Control of Discrete Systems (DCDS), 2009, p. 251-256Conference paper (Refereed)
    Abstract [en]

    We consider computer assisted troubleshooting of complex systems, where the objective is to identify the cause of a failure and repair the system at as low expected cost as possible. Three main challenges are: the need for disassembling the system during troubleshooting, the difficulty to verify that the system is fault free, and the dependencies in between components and observations. We present a method that can return a response anytime, which allows us to obtain the best result given the available time. The work is based on a case study of an auxiliary braking system of a modern truck. We highlight practical issues related to model building and troubleshooting in a real environment.

  • 41.
    Pernestål, Anna
    et al.
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Wettig, Hannes
    Complex Systems Computations Group, Department of Computer Science, Helsinki Institute for Information Technology, Finland.
    Silander, Tomi
    Complex Systems Computations Group, Department of Computer Science, Helsinki Institute for Information Technology, Finland.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Myllymäki, Petri
    Complex Systems Computations Group, Department of Computer Science, Helsinki Institute for Information Technology, Finland.
    A Comparison of Baysian Approaches to Learning in Fault Isolation2009In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344Article in journal (Other academic)
    Abstract [en]

    Fault isolation is the task of localizing faults in a process, given observations from it. To do this, a model describing the relations between faults and observations is needed.

    In this paper we focus on learning such models both from training data and from prior knowledge. There are several challenges in learning for fault isolation.

    The number of data and the available computing resources are often limited. Furthermore, there may be previously unobserved fault patterns.

    To meet these challenges we take on a Bayesian approach.

    We compare five different approaches to learning for fault isolation, and evaluate their performance on a real application, namely the diagnosis of an automotive engine.

  • 42.
    Svärd, Carl
    et al.
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
    A Mixed Causality Approach to Residual Generation Utilizing Equation System Solvers and Differential: Algebraic Equation Theory2008Report (Other academic)
    Abstract [en]

    The FDI approach to model-based diagnosis is considered. We present a method for residual generation that combines integral and derivative causality, and also utilizes equation system solvers and theory of differential-algebraic equation systems. To achieve this, a framework for computation of variables from sets of dependent differential and/or algebraic equations is introduced. The proposed method is applied to a model of the gas flow in an automotive diesel engine. The application clearly shows the benefit of using a mixed causality approach for residual generation compared with solely integral or derivative causality.

  • 43.
    Svärd, Carl
    et al.
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    An Observer-Based Residual Generation Method for Linear Differential-Algebraic Equation Systems2009In: European Journal of Control, ISSN 0947-3580, E-ISSN 1435-5671Article in journal (Other academic)
    Abstract [en]

    Residual generation for linear differential-algebraic systems is considered. A new systematic method for observer-based residual generation is presented. The proposed design method places no restrictions on the system to be diagnosed. If the fault 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 fault of interest. The method is iterative and relies only on constant matrix operations such as multiplications, null-space calculations and equivalence transformations, and thereby straightforward to implement. An illustrative numerical example is included, where the design method is applied to a nonobservable model of a robot manipulator.

  • 44.
    Svärd, Carl
    et al.
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Automated Design of an FDI-System for the Wind Turbine Benchmark2012In: Journal of Control Science and Engineering, ISSN 1687-5249, E-ISSN 1687-5257, Vol. 2012, no 989873Article in journal (Refereed)
    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.

  • 45.
    Svärd, Carl
    et al.
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    Residual Generators for Fault Diagnosis Using Computation Sequences With Mixed Causality Applied to Automotive Systems2010In: IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS, ISSN 1083-4427, Vol. 40, no 6, p. 1310-1328Article in journal (Refereed)
    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.

  • 46.
    Svärd, Carl
    et al.
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Frisk, Erik
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Realizability Constrained Selection of Residual Generators for Fault Diagnosis with an Automotive Engine Application2013In: IEEE Transactions on Systems, Man and Cybernetics: Systems, ISSN 2168-2216, Vol. 43, no 6, p. 1354-1369Article in journal (Refereed)
    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.

  • 47.
    Svärd, Carl
    et al.
    Scania CV AB, Södertälje, Sweden.
    Nyberg, Mattias
    Scania CV AB, Södertälje, Sweden.
    Frisk, Erik
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Krysander, Mattias
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Automotive engine FDI by application of an automated model-based and data-driven design methodology2013In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 21, no 4, p. 455-472Article in journal (Refereed)
    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.

  • 48.
    Svärd, Carl
    et al.
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Frisk, Erik
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Krysander, Mattias
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Data-Driven and Adaptive Statistical Residual Evaluation for Fault Detection with an Automotive Application2014In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 45, no 1, p. 170-192Article in journal (Refereed)
    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.

  • 49.
    Tundis, Andrea
    et al.
    University of Calabria, Italy.
    Rogovchenko, Lena
    Linköping University, Department of Computer and Information Science, PELAB - Programming Environment Laboratory. Linköping University, The Institute of Technology.
    Garro, Alfredo
    University of Calabria, Italy.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Fritzson, Peter
    Linköping University, Department of Computer and Information Science, PELAB - Programming Environment Laboratory. Linköping University, The Institute of Technology.
    Performing Fault Tree Analysis of a Modelica-Based System Design Through a Probability Model2013Conference paper (Refereed)
  • 50.
    Warnquist, Håkan
    et al.
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Nyberg, Mattias
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    A Heuristic for Near-Optimal Troubleshooting Using AO*2008In: Proceedings of the International Workshop on the Principles of Diagnosis, 2008Conference paper (Refereed)
    Abstract [en]

    When troubleshooting malfunctioning technical equipment, the task is to locate faults and make repairsuntil the equipment functions properly again. The AO* algorithm can be used to find troubleshootingstrategies that are optimal in the sense that the expected cost of repair is minimal. We have adaptedthe AO* algorithm for troubleshooting in the automotive domain with limited time. We propose a newheuristic based on entropy. By using this heuristic, near-optimal strategies can be found within a fixedtime limit. This is shown in empirical studies on a fuel injection system of a truck. In these results, theAO* algorithm using the new heuristic, performs better than other troubleshooting algorithms.

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