B.D. Netten

Bio: B.D. Netten is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Fault tree analysis & Consistency (database systems). The author has an hindex of 2, co-authored 2 publications receiving 8 citations.

Fault diagnosis of electronic systems using intelligent techniques: a review

Abstract: In an increasingly competitive marketplace system complexity continues to grow, but time-to-market and lifecycle are reducing. The purpose of fault diagnosis is the isolation of faults on defective systems, a task requiring a high skill set. This has driven the need for automated diagnostic tools. Over the last two decades, automated diagnosis has been an active research area, but the industrial acceptance of these techniques, particularly in cost-sensitive areas, has not been high. This paper reviews this research, primarily covering rule-based, model-based, and case-based approaches and applications. Future research directions are finally examined, with a concentration on issues, which may lead to a greater acceptance of automated diagnosis.

Large-Scale Fault Diagnosis for On-Board Train Systems

Abstract: A new approach is developed for fault diagnosis during different stages of development and operation of large train systems, incorporating case-based reasoning, conditional probabilities and indexing networks. Due to the size and complexity, the explicit, complete and accurate modelling of the on-board train systems is regarded impossible. The knowledge is implicitly available in fault-cases with possible symptoms, test results and actions. Off-line, different diagnostic systems are automatically maintained and (re)generated. Knowledge and experience of manufacturers and railway companies are fed back into all systems, but only after validation by authorised personnel. On-line, the system responses are consistent and fast enough, despite the size and uncertainty in the fault-cases. Available case-based reasoning tools have serious limitations in permissible size of the problem, handling probability factors, meeting required response times and satisfying the real-time requirements. The novelty of the proposed approach is that fault-networks, rather than fault-trees, are built automatically as the indexing structure of the case-base for on-line use.

Central advice system for fleet management and operations: improving the safety and reliability of rolling stock

Abstract: University of Wales Swansea - United Kingdom1 INTRODUCTIONDue to the changed role in current society, the requirements for mass transit products have changeddramatically over the last few years. The impact of social-economical changes lead to the necessity forpolyvalent service and technical staff. Technological evolution has made systems more complex, "intelligent"and interactive. On-board systems are therefore required to support higher comfort, increased availability,better maintainability, reduced life-cycle cost and increased efficiency.Safety, availability, reliability, maintainability and life cycle costs of transport systems are directly determinedby the efficiency in fault diagnosis throughout the life cycle. Improved efficiency in operational diagnosiscomprises;• Significant increase in the level of diagnosis automation.• Significant increase in the coverage of problems.• Significant increase in the accuracy of diagnosis by tuning symptoms, additional tests and actions.• Ensuring guaranteed response times for critical on-line diagnosis situations.To cope with the mentioned evolution, a Central Advice System (CAS) for rolling stock (trains, trams,underground, light-rail transport systems, etc.) is under development as a decision support system fordrivers, maintenance personnel and fleet managers. This development takes place within the framework ofthe European research and development project BRIDGE (ESPRIT Project 22154). BRIDGE specificallyaims at real-time diagnosis of very large technical applications. The overall goal is to significantly improvediagnosis efficiency in operations and reduce the efforts required in supporting actions.In this paper, the theoretical background of the BRIDGE tools will be discussed, the functionality of theCentral Advice System will be presented, followed by the application of BRIDGE in CAS.2 THE BRIDGE-PROJECTThe BRIDGE project aims specifically at real-time diagnosis for large technical applications. Two mainproblems are encountered due to the size and complexity of such applications. First of all, the definition ofthe domain knowledge requires attention. The relations between faults, failures, symptoms and repairactions can be fairly complex and sometimes unknown. The efforts for are related acquisition,representation and maintenance of this domain knowledge should be minimised. Secondly, the be able towork in real-time within the framework of embedded on-board systems, the search process for thediagnostic reasoning should be predictable and have a guaranteed response time.The BRIDGE tools comprise two separate tools; the Support Function Tool (SFT) and the OperationalDiagnosis Tool (ODT). The SFT is an off-line tool to support the acquisition and maintenance of theknowledge base. The knowledge representation is based on case-based reasoning (CBR); a reasoningtechnique that allows for a definition in the form of each fault independently in terms of symptoms, etc. CBRthen allows searching through these so-called cases to find the cause and to suggest corrective actions.Development support within the SFT includes the evaluations of integrity, coverage, accuracy and the real-time behaviour. After definition and analysis of the knowledge, and to generate from the definition a

Intelligent Systems Technology in the Fault Diagnosis of Electronic Systems

Abstract: With greater system complexity, shorter product life-cycles, lower production costs, and changing technologies, the need for intelligent tools for the diagnosis of electronic systems is becoming increasingly important. Ideally, failed products must be diagnosed as an integral part of manufacturing test, and field returns must be diagnosed and repaired in a cost effective manner.