On The Application of Domain Adversarial Neural Network to Fault Detection and Isolation in Power Plants

Abstract

In this paper, we introduce a new Fault Detection and Isolation (FDI) approach based on Transfer Learning (TL) for improving health monitoring systems of gas turbines under varying working conditions. Nowadays, researchers have found intelligent algorithms a reliable tool for condition monitoring of mechanical systems and processes. In this regard, modern automation systems in many industries, including power plants, are heavily utilizing machine learning algorithms. However, the performance of data-driven methods depends on the consistency of data distribution. Unfortunately, this assumption might not be satisfied with real-world problems. In this research, we contribute to finding a solution to this problem, which is a crucial barrier to many intelligent condition monitoring systems. We used domain adversarial training of neural networks to find models that can adapt to new working conditions of gas-turbines. Accordingly, a well-known gas-turbine simulator is employed to simulate the process behavior under various working conditions, and it is illustrated that even small variations in working conditions cause a dramatic decline in the performance of models. We demonstrate that the proposed TL-based FDI approach can be successfully employed to cope with the inconsistency of data distribution in process systems.