基于多域特征优化的航空发动机传感器智能故障诊断

In order to solve the problem of incomplete fault information reflected by single-domain features for aeroengine sensor fault diagnosis，a method based on optimized multi-domain features for intelligent fault diagnosis is proposed. The method extracts multi-domain features including time domain，frequency domain and morphological information，which together form multi-domain features to describe the health condition of the sensor from multiple dimensions. Afterwards，a new meta-heuristic algorithm，the boosted Henry gas solubility optimization（BHGSO）algorithm is proposed for feature selection to train the fault identification model with the lowest dimensional but knowledge-rich high quality feature information as much as possible to reduce the computational burden. Finally，intelligent fault diagnosis is performed using deep belief network（DBN）based on the feature vectors，which are used as indicators of the sensor’s health. The simulation results show that the proposed method can effectively diagnose faults in aeroengine sensors with high accuracy and low computational burden.