With the obvious characteristics of poor temporal logic in fault diagnosis and the strongly coupled feature parameters,the aero-engines working in the hostile gas path conditions of high temperature,pressure and strong vibration face with the degradation performance and structure defect problems such as fatigue and corrosion.And an aero-engine gas path fault diagnosis method based on temporal convolutional networks(TCN)and light gradient boosting machine(LGBM)is proposed to provide a feasible solution to the problems above.The diagnosis process can be divided into feature extraction and classification:TCN is introduced to guarantee the fault diagnosis training temporal logic and achieve the features fusion of distant layers and current layers,which is also strengthened by channel attention mechanism;the features are quickly classified based on LGBM model,and the Bayesian method is used to quickly optimize the model hyperparameters.Based on the aero-engine performance modelled by PROOSIS software,six types of fault mode are diagnosed and identified by taking a military low-bypass ratio turbofan engine as an example.The results indicate that the proposed model is effective for fault diagnosis and shows the superiority by comparing with other models.
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