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.

When a truck or bus suffers from a breakdown it is important that the vehicle comes back on the road as soon as possible. In this paper we present a prototype diagnostic decision support system capable of automatically identifying possible causes of a failure and propose recommended actions on how to get the vehicle back on the road as cost efficiently as possible.

This troubleshooting system is novel in the way it integrates the remote diagnosis with the workshop diagnosis when providing recommendations. To achieve this integration, a novel planning algorithm has been developed that enables the troubleshooting system to guide the different users (driver, help-desk operator, and mechanic) through the entire troubleshooting process.

In this paper we formulate the problem of integrated remote and workshop troubleshooting and present a working prototype that has been implemented to demonstrate all parts of the troubleshooting system.

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.

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.

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.

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.

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.

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.

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.