Aero-engine Sensor Fault Diagnosis Based on Convolutional Neural Network

In this paper, a deep model based on Convolutional Neural Network is proposed to solve the problem of sensor fault diagnosis for aero-engine. The model uses multiple convolutional layers to extract the features of the fault signal of the aero-engine sensor and uses the Softmax classifier to diagnose the fault. Through the comparison and analysis of the experiment, the accuracy of this method in fault diagnosis of aero-engine sensors can reach 100%. On the basis of this, the fault diagnosis method base on PCA-CNN is further proposed. Firstly, PCA is used to reduce the dimension of the original data, and then construct a Convolutional Neural Network for fault diagnosis. This method greatly improves the efficiency of the algorithm compared with CNN.