Gas-turbine condition monitoring using qualitative model-based diagnosis

Abstract

Gas turbines are critical to the operation of most industrial plants, and their associated maintenance costs can be extremely high. To reduce those costs and increase the availability of their gas turbines, plant operators have for many years relied on routine preventative maintenance-routinely checking and solving small problems before they grow into major ones. Recently, however, the power industry has moved sharply toward condition-based maintenance and monitoring. In this approach, intelligent computerized systems monitor gas turbines to establish maintenance needs based on the turbine's condition rather than on a fixed number of operating hours. By integrating several AI technologies-including qualitative model-based reasoning-the Tiger system significantly cuts costs and improves performance by using control-system information to perform condition monitoring for gas-turbine engines.