Fault Detection for Gas Turbines Based on Long-Term Prediction using Self-Organizing Fuzzy Neural Networks

For real-time condition monitoring and fault detection of dual-lane controlled systems, reduced order models and long-term prediction are required. In this paper fault detection of reduced order model of nonlinear systems based on long-term prediction is proposed by using self-organizing fuzzy neural network (SOFNN). The main advantages of SOFNN are that, firstly, it is very user friendly as it can automatically determine the model structure and identify the model parameters without requiring the in-depth knowledge about fuzzy systems and neural networks; secondly, it provides the excellent modeling accuracy. Data gathered at an aero engine test-bed serve as the test vehicle to demonstrate the long-term prediction. A fault detection system is designed by using SOFNN. SOFNN is trained and used to simulate system dynamic characteristic. The simulation result is compared with actual output, and then fault error is drawn. The simulation result shows that, SOFNN can simulate the system more accurately, thus the change of residual error is easy to be detected. This assures the validity of this fault detection system.