Turbine Engine Modeling Using Temporal Neural Networks for Incipient Fault Detection and Diagnosis

Abstract

We present a method of fault detection and diagnosis in turbine engines using temporal neural networks. Temporal neural networks allow us to represent the complete engine operating range by complementing the first-principle models which are usually restricted to takeoff and cruise phases. Because faults that are manifest only in particular phases can be detected, complete coverage leads to more accurate anomaly detection and fault diagnosis systems. The time series sensor data from the engine is collected during particular aircraft flight phases such as startup, takeoff, cruise, and shutdown. We use the echo state network to develop an incipient fault detection and diagnosis system. Echo state networks have several advantages over conventional types of temporal neural networks, including accuracy and ease of training. We demonstrate the efficacy of using the echo state networks to focus on flight phases that are difficult to model. We present results of our fault detection and diagnosis method with actual propulsion engine transient flight data.