QUALITATIVE MODELING AND FAULT DIAGNOSIS OF DYNAMIC PROCESSES BY MIDAS†

Abstract

The Model Integrated Diagnostic Analysis System (MIDAS) is a program for diagnosing abnormal transient conditions in chemical, refinery, and utility systems. MIDAS employs causal reasoning using an event model derived from piping and instrumentation diagrams, and from quantitative process models. Root causes typically considered are equipment degradation and failure, incorrect manual actions, and external disturbances. By prompt and accurate diagnosis of these conditions, MIDAS can reduce the risk of safety hazards, material wastage, and unnecessary downtime.

This paper extends and complements a previous report on MIDAS† by detailing the qualitative modeling techniques used in MIDAS, and presenting the results of a simulation case study. The modeling methodology employs a new result on qualitative modeling to help resolve ambiguity from feedback loops and locate apparent “non-local causality”. The event model allows MIDAS to exploit the sequence of malfunction propagation in its internal reasoning. The models include control system responses and MIDAS uses this knowledge to correctly identify malfunctions even when the primary symptoms are concealed by control system compensations. The reasoning methodology used to perform the diagnosis is process independent.

The case study reported involves diagnosis of a reactor-heat exchange process with associated control systems, with 10 sensors and over 100 potential malfunctions. The results show that MIDAS includes in its hypothesis set the correct malfunction 98.9% of the time. Of the scenarios tested, 40% exhibited control system compensation, 20% exhibited inverse response, and over 30% included out-of-order events.

KEYWORDS:

• Fault diagnosis
• Causal reasoning
• Process safety
• Alarm analysis
• Deep knowledge expert systems

Notes

†Finch, F.E., M.A. Kramer, AIChE Spring National Meeting, Houston, TX (1989).