An Approach to Intelligent Fault Diagnosis of Cryocooler Using Time-Frequency Image and CNN

Abstract

Cryocooler plays an essential role in the field of infrared remote sensing. Linear compressor, as the power component of the cryocooler, will directly affect the normal operation and performance of the detector if there is a fault. Therefore, the intelligent fault diagnosis of the linear compressor is of great significance. An intelligent fault diagnosis method based on time-frequency image and convolutional neural network is proposed to solve the problems of piston and cylinder friction, mass imbalance, and plate spring distortion in the linear compressor. Firstly, the wavelet transform time-frequency analysis method is used to generate the corresponding time-frequency image. Convolutional neural network (CNN) is used to automatically extract features of time-frequency images, so as to realize the classification of various fault modes. The results of simulation experiments show that the method can identify several fault modes of the linear compressor with 95% accuracy.