A new method for fault detection of aero-engine based on isolation forest. (November 2021)

• Record Type: Journal Article
• Authors: Wang, Hongfei; Jiang, Wen; Deng, Xinyang; Geng, Jie
• Is Part Of: Measurement. Volume 185(2021)
• Publication Date: 2021-11
• Subjects: Turbofan engine -- Gas path system -- Fault detection -- Isolation forest; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.110064
• Languages: English
• ISSNs: 0263-2241

Abstract

Abstract: The research on fault detection of aero-engine is of great significance to its safe and reliable operation. In this paper, a dynamic threshold method for aero-engine fault detection based on Isolation Forest ( i Forest) is proposed. The proposed method can use only normal aero-engine data for training to build the fault detection model, which solves the problem that there is no large amount of fault data for training in the field of aero-engine fault detection due to the limitations of actual conditions. The method is verified by the residual data of the turbofan engine gas path system which is generated by the state variable model under three different fault states. Compared with the results of other methods, it is found that the proposed method can not only achieve high detection accuracy but also has a short running time. It is proved that the proposed method is suitable for fault detection of aero-engine. Highlights: An effective Aero-engine fault detection model based on Isolation Forest. The proposed model can construct an adaptive dynamic threshold. The proposed model can not only achieve high detection accuracy but also has a short running time.