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 (iForest) 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.
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