Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China.
University of the Chinese Academy of Sciences, Beijing 100049, China.
Comput Intell Neurosci. 2022 May 2;2022:1754726. doi: 10.1155/2022/1754726. eCollection 2022.
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.
在红外遥感领域，致冷机起着至关重要的作用。线性压缩机作为致冷机的动力元件，如果发生故障，将直接影响探测器的正常运行和性能。因此，对线性压缩机进行智能故障诊断具有重要意义。针对线性压缩机中出现的活塞与缸体摩擦、质量不平衡、板簧变形等故障问题，提出了一种基于时频图像和卷积神经网络的智能故障诊断方法。首先，利用小波变换时频分析方法生成相应的时频图像，再采用卷积神经网络（CNN）自动提取时频图像的特征，从而实现对各种故障模式的分类。仿真实验结果表明，该方法能够以 95%的准确率识别线性压缩机的几种故障模式。
