Affiliations: Laboratory for Artificial Intelligence, University of Hamburg, Vogt‐Koelln‐Str. 30, 22527 Hamburg, Germany E‐mail: {milde, neumann}@informatik. uni‐hamburg.de | Fraunhofer‐Institut IITB, Fraunhoferstr. 1, 76131 Karlsruhe, Germany E‐mail: [email protected] | ESG Elektroniksystem‐ und Logistik‐GmbH, Germany E‐mail: malik@esg‐gmbh.de | Technical University of Munich, Department of Computer Science, Orleansstr. 34, 81667 Munich, Germany E‐mail: [email protected]
Note: [] Corresponding author.
Note: [] Also affiliated with Robert Bosch GmbH during the project.
Abstract: Although the area of model‐based diagnosis has developed a number of prototypes with impressive features that promised economic impact and, hence, caught industrial interest, the number of actual industrial applications is still close to zero. One of the reasons is that the successful techniques have not yet been turned into tools that reflect and support the current diagnostic work processes and their existing tools. The INDIA project joined eight German partners (research groups, software suppliers, and end users) in an attempt to take a major step in the transfer of model‐based diagnosis techniques into industrial applications. This paper describes part of the work carried out in this project. Rather than presenting the theoretical foundations of the techniques in depth, we focus on the aspect of how model‐based diagnostic techniques can be related to established tools and systems in order to provide some leverage for today’s work processes and to change them gradually, as opposed to postulating a radical change in current practice and organizational structures. From this perspective, we discuss the utilization of model‐based techniques for the generation of fault trees for on‐line testing and diagnosis of fork lifters, generation of test plans for an intelligent authoring system for car diagnosis manuals, and the exploitation of existing state‐chart process descriptions for post‐mortem diagnosis of processes in a dyeing plant.
