); [EMAIL_ADDRESS] (J.G.) Zhej Sun  
1 School of Education, Zhejiang University of Technology, 288 Liuhe Road, Hangzhou 310023, China; [EMAIL_ADDRESS] (H.J. ); [EMAIL_ADDRESS] (J.G.) [PERSON]
equipment. Therefore, it is necessary to diagnose the hidden faults and manufacturing defects of the assembled compressor in the production inspection link. At the same time, the type of manufacturing defect of compressors can be diagnosed, and the cause of the defect can be found. Lastly, the automated production line can be optimized. In the past, fault diagnosis of hermetic refrigeration compressors often relied on manually identifying abnormalities by touching and hearing. This method is not only wasting manpower, but also makes it difficult to ensure accuracy. To solve this issue, a large number of researchers have studied intelligent fault diagnosis methods for hermetic refrigeration compressors. [PERSON] et al. [7] used information entropy to analyze signal characteristics and used support vector machines (SVM) to diagnosis the fault of the compressor valve. [PERSON] et al. [8] used infrared thermal imaging and SVM classifiers to diagnose reciprocating compressors. [PERSON] et al. [9] used multiscale sample entropy and global distance for compressor fault diagnosis. These methods can effectively diagnose the type of compressor failure. However, these methods not only require a large amount of a priori knowledge and take a lot of time, but also cannot handle complex data and big data. Since 2010, with the rapid development of data-driven methods represented by deep learning, the above issues have been easily solved. [PERSON] et al. [10] realized compressor fault diagnosis with the help of a deep belief network and used ensemble empirical mode decomposition to denoise the original signal. [PERSON] et al. [11] used long- and short-term memory networks to diagnose compressor faults and used a Bayesian network to optimize the parameters of the model. [PERSON] et al. [12] constructed deep convolutional neural networks (CNN) to diagnose the fault of the reciprocating compressor air valve. In this paper, CNN are introduced to automatically learn different feature information from the signal of a hermetic compressor for fault classification. In recent years, many studies have proposed intelligent fault diagnosis methods for rotating machinery based on vibration signals. [PERSON] et al. [13] used raw vibration signals for fault identification of the fan. [PERSON] et al. [14] proposed a deep decoupled CNN input as the raw signal to diagnose faults. The time series does not represent the frequency characteristics and displacement variation characteristics of the raw vibration signal. Therefore, a time-frequency processing method is introduced to represent the time-domain and frequency-domain characteristics of the raw vibration signal. [PERSON] et al. [15] used wavelet transform to extract vibration signal features and used neural networks to classify the fault of reciprocating compressors. [PERSON] et al. [16] used the time-frequency method for extracting features and used logistic regression and SVM to diagnose the fault of the compressor valve. [PERSON] et al. [17] proposed a comparative analysis of the continuous wavelet transform (CWT) and the Hilbert transform while using a genetic algorithm for fault diagnosis of asynchronous motors. [PERSON] et al. [18] compared the feature extraction capabilities of short-time Fourier transforms (STFT), CWT and Hilbert-Huang transform (HHT), and finally used CNN for fault diagnosis. These methods demonstrate that the time-frequency processing method has strong feature extraction capability. The time-frequency processing method can improve the stability of the diagnosis algorithm. It is difficult to find a method with absolute advantages because different time-frequency processing methods have different advantages for the compressor vibration signals.