Fault Diagnosis of Aeroengine Control System Sensor Based on Optimized and Fused Multidomain Feature

Abstract

Improving the efficacy and dependability of aeroengines requires timely and effective sensor fault diagnosis. Deep learning-based fault diagnosis method is a current research hotspot. To overcome some of the method's existing shortcomings and improve the reliability of fault diagnosis, this paper proposes a novel intelligent fault diagnosis framework with higher quality features and more effective fault classifiers. The proposed plan includes three stages. Firstly, multidomain features (time and frequency domain features) are extracted to describe the sensor's health from several dimensions. Secondly, the advanced Henry gas solubility optimization algorithm (HGSO) is applied to improve classification accuracy through feature selection, and the operating conditions and the features extracted by the network are fused as fault indicators. Finally, an adaptive deep belief network (ADBN) with relu-softsign combination activation layers, variable learning rate, and optimized network structure is proposed as the fault identifier. The advantages of the first two stages lie in the complete utilization of information and reducing the data dimension. In addition, the detection performance and the convergence speed is enhanced by the proposed ADBN. The experimental data are derived from a combination of measured and simulated data generated from the aeroengine model. The experimental results indicate that the improved method can produce better performance and outcomes than the unimproved methods for all fault scenarios, with a higher diagnostic accuracy of 98.1% and a reduced time of 98 s. The efforts of this study provide a efficient and adaptable way to aeroengine sensor fault diagnosis.