关键词: 故障诊断, 神经网络, 模式识别, 自动编码, 电信号, 梯度下降法

Abstract: This paper designs an intelligent classification system based on artificial neural networks. The purpose is to solve the problem of multiple data，arduous task in the aircraft test and intellectualize the management of the testing work. In this way, the system can reduce the workload and the reliance on testing experience as well as can store the nonlinear debugging experience in the form of expert database. This system has many deficiencies, such as, long training time and highly dependent the initial threshold. To this end, the paper uses principal component analysis to compress the raw data and Auto-Encoder that has been successfully applied in deep learning to initialize the network weights. Experimental data indicate: Compared with traditional methods, accuracy, stability and response speed of the improved learning system were significantly increased.

Key words: Fault Diagnosis, ANN, Pattern Recognition, Auto-Encoder, electronic signal, Gradient descent method