ENHANCING OFF-LINE AND ONLINE CONDITION MONITORING AND FAULT-DIAGNOSIS

This paper describes the use of artificial intelligence technology to enhance offline and on-line condition monitoring and fault diagnosis a nd to integrate these two developments into a closed-loop diagnostic t ool for complex systems in modern transportation. Two developments are presented here, which were verified in industrial diagnostic problems ; on-line fault diagnosis for trains, and off-line aircraft Engine Con dition Monitoring (ECM). Case-based reasoning (CBR) is used to incorpo rate the knowledge and experience of both train manufacturers and rail way companies for on-line train fault diagnosis. The size of the diagn ostic problem is such that explicit formulation of fault-trees is almo st impossible. CBR facilitates the automatic generation, consistency c hecking and maintenance of the fault-trees. Additional measures have b een taken to meet the real-time requirements for on-line use of a CBR- based diagnostic system. A balanced combination of neural networks and expert-system techniques is used to ensure more consistent off-line E CM. The information, such as trends from in-flight measured aircraft a nd engine parameters, crew trouble reports, maintenance and findings f rom accessory repair shops, can be incorporated to assess the engine's state of health, and to deduce appropriate preventative or corrective actions.