Mattias Nyberg

An Efficient Algorithm for Finding Minimal Overconstrained Subsystems for Model-Based Diagnosis

In model-based diagnosis, diagnostic system construction is based on a model of the technical system to be diagnosed. To handle large differential algebraic imemodels and to achieve fault isolation, a common strategy is to pick out small overconstrained parts of the model and to test these separately against measured signals. In this paper, a new algorithm for computing all minimal overconstrained subsystems 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.

Model-based diagnosis of an automotive engine using several types of fault models

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.

Model based diagnosis of the air path of an automotive diesel engine

A model based diagnosis system for the air-path of a turbo-charged diesel engine with EGR is constructed. The faults considered are air mass-flow sensor fault, intake-manifold pressure sensor fault, air-leakage, and the EGR-valve stuck in closed position. A non-linear engine model, with four states, is constructed. The diagnosis system is then constructed in the framework of structured hypothesis tests and by using observers estimating unknown fault-parameters. To handle modeling errors a new method for adaptive thresholds is proposed. The diagnosis system is successfully evaluated in a real car driving on the road.

Brief A minimal polynomial basis solution to residual generation for fault diagnosis in linear systems

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.

Residual Generators for Fault Diagnosis Using Computation Sequences With Mixed Causality Applied to Automotive Systems

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.

Model Based Diagnosis for the Air Intake System of the SI-Engine

Because of legislative regulations like OBDII, on-board diagnosis has gained much interest lately. A model based approach is suggested for the diagnosis of the air intake system of an SI-engine. Im ...

Criterions for detectability and strong detectability of faults in linear systems

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.

Automated Design of an FDI-System for the Wind Turbine Benchmark

Present paper proposes an FDI-system for the wind turbine benchmark designed by application of a generic automated design method, in which the number of required human decisions and assumptions are ...

Minimal Structurally Overdetermined Sets for Residual Generation: A Comparison of Alternative Approaches

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.

Residual Generation for Fault Diagnosis of Systems Described by Linear Differential-Algebraic Equations

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