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.

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.

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.

To improve safety, reliability, and efficiency of automotive vehicles and other technical applications, embedded systems commonly use fault diagnosis consisting of fault detection and isolation. Since many systems are constructed as distributed embedded systems including multiple control units, it is necessary to perform global fault isolation using for example a central unit. However, the drawbacks with such a centralized method are the need of a powerful diagnostic unit and the sensitivity against disconnections of this unit.

Two alternative methods to centralized fault isolation are presented in this thesis. The first method performs global fault isolation by a istributed sequential computation. For a set of studied systems, themethod gives, compared to a centralizedmethod, amean reduction inmaximumprocessor load on any unitwith 40 and 70%for systems consisting of four and eight units respectively. The second method instead extends the result of the local fault isolation performed in each unit such that the results are globally correct. By only considering the components affecting each specific unit, the extended result in each agent is kept small. For a studied automotive vehicle, the second method gives, compared to a centralized method, a mean reduction in the sizes of the results and the maximum processor load on any unit with 85 and 90% respectively.

To perform fault diagnosis, diagnostic tests are commonly used. If the additional evaluation of tests can not improve the fault isolation of a component then the component is ready. Since the evaluation of a test comes with a cost in for example computational resources, it is valuable to minimize the number of tests that have to be evaluated before readiness is achieved for all components. A strategy is presented that decides in which order to evaluate tests such that readiness is achieved with as few evaluations of tests as possible.

Besides knowing how fault diagnosis is performed, it is also interesting to assess the effect that fault diagnosis has on for example safety. Since fault tree analysis often is used to evaluate safety, this thesis contributes with a systematic method that includes the effect of fault diagnosis in fault trees. The safety enhancement due to the use of fault diagnosis can thereby be analyzed and quantified.

Safety is of major concern in many autonomous functions in automotive systems and aerospace. In these application areas, it is standard to use fault trees, and a natural question in many modern systems that include sub-systems like diagnosis, fault-tolerant control, and autonomous functions is how to include the performance of these algorithms in a fault tree analysis for safety. Many possibilities exist but here a systematic way is proposed. It is shown both how safety can be analysed and how the interplay between algorithm design in terms of missed detection rate and false alarm rate is included in the fault tree analysis. Examples illustrate analysis of diagnosis system requirement specification and algorithm tuning. Copyright © 2006 John Wiley & Sons, Ltd.

Safety is of major concern in many applications such as in automotive systems and aerospace. In these applications it is standard to use fault trees, and a natural question in many modern systems that include sub-systems like diagnosis, fault tolerant control and autonomous functions, is how to include the performance of these algorithms in a fault tree analysis for safety. Many possibilities exist but here a systematic way is proposed. It is shown both how safety can be analyzed and how the interplay between algorithm design in terms of missed detection rate and false alarm rate is included in the fault tree analysis. Examples illustrate analysis of diagnosis system requirement specification and algorithm tuning.

Fault diagnosis is becoming increasingly important for many technical systems. This is for example true in automotive vehicles where fault diagnosis is needed due to economic reasons such as efficient repair and fault prevention, and legislations that mainly deal with safety and pollution. The objective for a diagnostic system is to detect and isolate faults in the system. A diagnostic system consists of several specialized parts, for example residual generators, diagnoses calculation, and communication with other systems.

In embedded systems with dozens of electronic control units that individually states local diagnoses, it can be computationally expensive to find which combination of local diagnoses that points at the correct set of faulty components. A distributed method is proposed where local diagnoses are extended using networked information. The extension is done thru the sharing of local conflicts or local diagnoses between the electronic control units. The number of global diagnoses grows with the number of local diagnoses. Therefore, an algorithm is presented that from the local diagnoses calculates the more likely global diagnoses. This restriction to the more likely diagnoses is sometimes appropriate since there are limitations in processing power, memory, and network capacity.

A common approach to design diagnostic systems is to use residual generators, where each residual generator is sensitive to some faults. A method is presented that constructs residual generators from sets of overdetermined model equations, such that simulation can be used to determine if the residual is zero or not. The method thus avoids the need to analytically transform the set of equations into some specific residual generator form. It can also utilize smaller sub sets of equations like minimally overdetermined sets, and it can further take advantage of object-oriented simulation tools.