Fault detection and isolation of gas turbine engines using a bank of neural networks

• Fault detection and isolation of a dual spool gas turbine engine is investigated by using dynamic neural networks.
• Banks of dynamic neural nets and time-delay neural nets are developed for solving the fault detection problem.
• Results show certain engine variables have better detection capabilities as compared to others.
• Fault detection performance was shown to be improved by monitoring several engine variables simultaneously.
• Fault isolation uses multilayer perceptions as pattern classifier of residual signals that are generated from the fault detection phase.

The main goal of this paper is to design and develop a fault detection and isolation (FDI) scheme for aircraft gas turbine engines by using neural networks. Towards this end, first for the fault detection task two types of dynamic neural networks are used and compared to learn the engine dynamics. Specially, the dynamic neural model (DNM) and the time delay neural network (TDNN) are utilized. For both architectures a bank of neural networks is trained separately to capture the dynamic relationships among the engine measurable variables. The results show that certain engine parameters have better detection capabilities as compared to the others. Finally, the fault isolation task is accomplished by using a multilayer perception (MLP) network functioning as a pattern classifier applied to the residual signals that are generated by the two dynamic neural networks used for the purpose of the fault detection task. The simulation results do indeed substantiate and verify that our proposed FDI scheme represents a promising tool for aircraft engine diagnostics and health monitoring.