The goal of gas turbine performance diagnositcs is to accurately detect, isolate, and assess the changes in engine module performance, engine system malfunctions and instrumentation problems from knowledge of measured parameters taken along the engine’s gas path. The method has been applied to a wide variety of commercial and military engines in the three decades since its inception as a diagnostic tool and has enjoyed a reasonable degree of success. During that time many methodologies and implementations of the basic concept have been investigated ranging from the statistically based methods to those employing elements from the field of artificial intelligence. The two most publicized methods involve the use of either Kalman filters or artificial neural networks (ANN) as the primary vehicle for the fault isolation process. The present paper makes a comparison of these two techniques.

Issue Section:
Gas Turbines: Controls, Diagnostics, and Instrumentation
Keywords:
Kalman filters, neural nets, gas turbines, performance evaluation, fault diagnosis
Topics:
Artificial neural networks, Kalman filters, Engines, Gas turbines
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