## 1 Introduction
In order to deal with the transient characteristics of the signal, some scholars have proposed other methods to extract the faulty feature of the vibration signal of the reciprocating compressor. A feature extraction method of local maximum multi-scale entropy and extended multi-scale entropy was proposed by [PERSON] et al. [15]. The extracted features can characterize bearing faults well. [PERSON] et al. [16] proposed the Teager-Kaiser energy operator to simultaneously extract the vibration signal, pressure signal, and current signal of the reciprocating compressor and then used the deep belief network to identify the fault. [PERSON] et al. [17] proposed adaptive peak decomposition to extract the characteristics of the vibration signal of the four-state reciprocating compressor. [PERSON] et al. [18] sparsely encoded the reciprocating compressor operating data in 5 years and identified the faults using SVM. [PERSON] et al. [19] calculated the normalized Lempel-Ziv complexity of the signal using the mean symbolization method and applied artificial neural networks to diagnose the unit fault. The intelligent fault diagnosis of the reciprocating compressor includes two steps: Feature extraction and pattern recognition. Features are mainly extracted in the time domain, frequency domain, and time-frequency domain. The effectiveness of feature extraction directly affects the subsequent diagnosis results. Because the reciprocating compressor contains many rotating and moving parts, it would cause many vibration sources and the measured vibration signal usually includes transient vibration and noise. As a result, the frequency bandwidth of the signal has a complicated shape as well as showing nonlinear characteristics. Hence, conventional intelligent fault diagnosis methods are often not suitable for reciprocating compressors and affect the accuracy of failure mode identification. In recent years, as the concept of deep learning has been proposed, it has been widely used in various fields because deep learning can effectively self-extract features and classify data. It is also widely used in machine fault diagnosis. In some more complex mechanical systems, it can extract the good characteristics of system faults and then perform fault classification or predict the remaining life of the machine through regression. [PERSON] [20] used a deep neural network model with an adaptive learning ability and hierarchical learning rate, of which the testing accuracy was over 99.3% in the bearing data set. Long- and short-term memory (LSTM) is very suitable for processing time series. It is a kind of deep feedback neural network, which can effectively extract the characteristics of short and long periods of signal. Therefore, it has been applied to fault diagnosis by many scholars. [PERSON] et al. [21] proposed LSTM based on deep learning, which directly extracts the original vibration signal features of the reciprocating compressor and performs fault pattern recognition. He used Bayesian optimization to select the hyperparameters of the model and compared with it several machine learning methods. The comparison showed that his method obtained better diagnostic results. [PERSON] [22] proposed a network called SeriesNet, which is specially used to process time series. It consists of an LSTM network and dilated causal convolutional neural network. It effectively predicts several stock and regional temperature data sets and predicts the results. It is more consistent than the typical ANN, SVM, and other prediction models. The convolutional neural network (CNN) is different from LSTM. It is a kind of feedforward neural network. It pays more attention to the local characteristics of data, and the effectiveness of the CNN has been proved in the field of fault diagnosis. [PERSON] et al. [23] applied a CNN with a LeNet-5 architecture to convert the original vibration signal into an image. Furthermore, he used this model for feature self-extraction and pattern recognition.