A Practical Methodology for Generating Diagnosis gz Recovery Applications in Large-Scale Plants *

Making manufacturing systems more robust i.e. able to carry out their function under the presence of faults, is an issue of paramount importance. This issue involves on-line real-time detection of faults, diagnosis of faults, and recovery from the faults. In this paper we present a methodology that is able to generate practical and efficient solutions to this problem. Our methodology is available as part of IPCS, which is a software package used for generating on-line real-time applications for large-scale plants. The basic fault modeling methodology enables a user to specify probability and time weighted causal relationships between equipment and process failure-modes. Fault detection is specified in terms of violation conditions for alarms and alarm failure-mode associations. Polynomial complexity graph algorithms enforce structural, probabilistic, and temporal constraints on the fault model and the set of ringing alarms for generating a diagno-