Gas Path Fault Diagnosis of Turboshaft Engine Based on Novel Transfer Learning Methods

Abstract Data-driven fault diagnosis method is widely used in the field of engine health management, which uses engine sensor data as input and engine faulty components as output for component-level fault diagnosis of the engine. The application premise of the general data-driven fault diagnosis method is that all data come from the same working conditions, that is, they belong to the same distribution. However, this assumption is not valid in the actual engine fault diagnosis, because the engine state will change with the increase of running time. In the meantime, collecting engine data is usually expensive, time-consuming, and laborious. To solve these problems, extreme learning machine (ELM)-based two transfer learning methods for fault diagnosis of turboshaft engines are proposed in this paper. One is joint solving ELM (JSELM), which regards the information of the target domain and source domain as similar and different parts, respectively, and knowledge is extracted from them at the same time. The other is model transfer-based ELM (MTELM), which uses the idea of pretraining. First, a general ELM classifier is trained with the source domain data and then fine-tuned with the target domain data. Both methods have a good real-time performance as the traditional ELM. When there are a few data in the target domain, they achieve much better classification accuracy than traditional ELM. Finally, experiments are carried out with turboshaft engine simulation data. The results show that both methods are effective, especially MTELM, which has better classification accuracy.