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Model Based Fault Diagnosis

Participating Associate Professor: Erik Frisk.
Participating Assistant Professors: Mattias Krysander, Mattias Nyberg, and Jan Åslund.

Projects are financed through Swedish Research Council, Programrådet för fordonsforskning, TurboPower, MOVIII, CENIIT, VISIMOD, and NFFP4.

Industrial partners are Sörman, Saab Aerosystems AB, Siemens Industrial Turbomachinery, Saab Automobile AB, Mecel AB, ABB Automation Systems, Scania and Volvo Technology.

Are you a student, interested in doing a master thesis in diagnosis? Take a look at our master thesis page (in Swedish) or contact us for further information.

What is Diagnosis?

From a general perspective, including both the medical and technical case, diagnosis can be explained as follows. For a process there are observed variables or behaviors for which there is knowledge of what is expected or normal. The task of diagnosis is to, from the observations and the knowledge, generate a diagnosis, i.e. to decide whether there is a fault or not and also to identify the fault. The following picture shows the information flow including a process and a diagnosis system.

Diagnosis application

For a more elaborate introduction, see our popular introduction to diagnosis.

Diagnosis Research at Vehicular Systems

Diagnosis research at the group is currently performed within a few areas. Below is a brief description of these topics together with links to publications within each area.

A complete list of our diagnosis publications is found from the groups main publication page.

Design and analysis of linear residual generators

In any diagnosis system it is necessary to generate fault sensitive signals, residuals, with which the diagnosis system can draw conclusions about possible fault states of the supervised process. The aim of our research within residual generation is to develop methods to generate such residuals based on a mathematical description of the process. Fundamental difficulties are noise and uncertainties in the model descriptions and a main objective is to systematically handle these problems.

The problem is highly dependent on the type of mathematical description of the process. A well studied class of models, for which far reaching results are possible, are linear dynamic models. We have studied how process models consisting of ordinary differential equations (ODE), or more general differential-algebraic equations (DAE) can be used to generate residuals and analyse detectability etc. Both cases when the model is assumed perfect and uncertain has been studied. Uncertain models mean models subjected to stochastic noise or parametric uncertainties. In those cases, the design may end up in an optimization problem where a trade-off is done between fault sensitivity and sensitivity to the model uncertainties.

Design and analysis of non-linear residual generators

Nearly all systems exhibit non-linear behavior and to get optimal performance of the diagnosis system, all system knowledge need to be taken into account during design. Thus, it is desirable that also non-linear process models can be considered when designing residual generators. This introduces additional difficulties compared to the linear problem, e.g. how to handle noise and uncertainties. Common tools are quite naturally parameter identification techniques, elimination theory and state observation techniques.

Fault isolation

If a fault is detected then the process of identifying the fault is called fault isolation. Isolation can be achieved by using a set of tests that are sensitive for different sets of faults. The goal of our research within fault isolation is to construct a diagnosis system with the highest possible isolation capability. To obtain this goal the following issues are currently the topic of our research: Which set of tests needs to be designed? How are the decisions of each test defined? and how is the diagnosis statement computed? To work with these questions formally, a framework for diagnosis is developed.

Structural methods

Diagnosis performance regarding fault detectability and isolability might be required in order to meet for example safety and environmental protection requirements for the overall process. To be able to perform model based supervision, some redundancy is needed and this redundancy can be provided by some sensors together with a model description of the behavior of the process. Natural questions are what isolability that can be achieved given a model describing a process equipped with a fixed set of sensors or if the sensors selection is part of the problem, to decide which sensors to include in the process in order to achieve a required fault isolability. For large complex models, it is suitable to answer these questions using algorithms analysing only the structural properties of the model, i.e. which variables that are included in each equation. The development of such algorithms is a main research topic here.


Although our research have a clear theoretical focus, it is of central importance to keep a high level of industrial relevance in our research. Thus, application studies are fundamental to get hands-on experience on how our methods perform in practice and get feedback when identifying research topics that are both industrially and academically relevant. Our application studies has predominately been in the automotive area, mainly because of longstanding collaborations with automotive industry and our automotive research laboratory which makes real life testing of diagnostic algorithms possible.

Informationsansvarig: Erik Frisk
Senast uppdaterad: 2010-11-29