全封闭往复式压缩机制造缺陷诊断方法研究

As the core component of the refrigeration system, the quality of hermetic reciprocating compressor determines the energy efficiency level, silent effect, and product life of the whole system. In the manufacturing process of the production line, in order to solve the shortcomings that it is difficult to identify the defective product due to the characteristics of the hermetic structure, this paper proposes a diagnosis method for manufacturing defects based on the vibration signal of the compressor shell. First, the ensemble empirical mode decomposition (EEMD) is used to spectrally decompose the vibration signal. Furthermore, the multiscale sample entropy (MSE) is utilized to characterize the complexity of each intrinsic mode function (IMF) at different scales, and the values are used as the feature vector. Finally, the support vector machine (SVM) is used to complete the classification of manufacturing defects. Experimental results show that the detection the proposed method can accurately identify and classify typical manufacturing defects, and provide relevant theoretical and testing base for the online detection of hermetic refrigeration compressors.