Combating Engine Failures in Tanks.

• Position: Using artificial intelligence to find problems - Brief Article

Artificial intelligence could boost the Army's battle against budget crunching as well as aid battlefield readiness by diagnosing engine problems in tanks before costly repairs are needed. Researchers at the Department of Energy's Pacific Northwest National Laboratory, Richland, Wash., are developing Turbine Engine Diagnostics Using Artificial Neural Networks (TEDANN). The technology uses diagnostic engineering, artificial neural networks, and model-based deck sion algorithms to predict failures and abnormal operations in the M1 Abrams main battle tank's turbine engine.

Currently, sensors aboard a tank indicate only if the engine's operations are in or out of tolerance--that is, if a problem does or doesn't exist. TEDANN monitors various engine conditions continually and tracks potential deviations from normal operations. Through TEDANN's predictive diagnostics, maintenance personnel could be alerted to adverse conditions as they develop, potentially reducing expensive engine failures.

The Army schedules periodic engine checks on M1 tanks that require the engine to be removed from the hull. Utilizing TEDANN, these would be performed automatically during normal operations. Mechanics would know if an engine needs maintenance before its scheduled service date or if it is in good health and doesn't need servicing.

TEDANN utilizes data from 32 existing sensors on the...