Integrating Model-based Diagnosis Techniques into Current Work Processes - Three Case Studies from the INDIA Project

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.

[1]  Gregory Provan,et al.  Modeling and diagnosis of timed discrete event systems-a factory automation example , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[2]  Gianfranco Lamperti,et al.  Diagnosis of Large Active Systems , 1999, Artif. Intell..

[3]  Marie-Odile Cordier,et al.  Event-Based Diagnosis for Evolutive Systems , 1994 .

[4]  Stéphane Lafortune,et al.  Failure diagnosis using discrete event models , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[5]  Peter Struss,et al.  Meeting Re-use Requirements of Real-Life Diagnosis Applications , 1999, XPS.

[6]  Stéphane Lafortune,et al.  Active diagnosis of discrete event systems , 1997, Proceedings of the 36th IEEE Conference on Decision and Control.

[7]  Brian C. Williams,et al.  Diagnosing Multiple Faults , 1987, Artif. Intell..

[8]  P. Struss Problems of interval-based qualitative reasoning , 1989 .

[9]  David Harel,et al.  Modeling Reactive Systems With Statecharts : The Statemate Approach , 1998 .

[10]  Johan de Kleer,et al.  Readings in qualitative reasoning about physical systems , 1990 .

[11]  Bruce P. Douglass,et al.  Doing hard time: developing real-time systems with uml , 1999 .

[12]  Neal Snooke,et al.  Dynamic analysis of qualitative circuits for failure mode and effects analysis , 1996, Proceedings of 1996 Annual Reliability and Maintainability Symposium.

[13]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[14]  Jan Lunze,et al.  Process Diagnosis Based on a Discrete-Event Description , 1999 .

[15]  Martin Polke,et al.  Das Informationsmodell: Basis für die interdisziplinäre Prozeßbeschreibung , 1994 .

[16]  Ralph Johnson,et al.  design patterns elements of reusable object oriented software , 2019 .

[17]  Peter Struss,et al.  Testing Physical Systems , 1994, AAAI.

[18]  Chris Price,et al.  Interpreting Simulation with Functional Labels , 1996 .

[19]  Bernd Neumann,et al.  Qualitative Reasoning about Electrical Circuits using Series-Parallel-Star Trees , 1996 .

[20]  Lothar Hotz,et al.  Facing Diagnosis Reality-Model-Based Fault Tree Generation in Industrial Application , 2000 .