## 1 Introduction  
A reciprocating compressor is the most widely used compressor type in industry and key equipment in gas transmission pipelines, petrochemical industry, fertilizer industry, oil refinery, ethylene chemical industry, coal chemical industry, and other industries. Monitoring and fault diagnosis of reciprocating compressors can help the machine to continue normal operation and it is of great significance. For reciprocating compressors, the gas valve is one of the components with the highest failure rate in the reciprocating compressor in [1]. Research on the fault diagnosis of the reciprocating compressor valve mainly focuses on three aspects: Vibration monitoring, thermal performance monitoring, and the indicator diagram. 1. Vibration monitoring method: Gas valve faults often occur together with abnormal vibration signal. Therefore, using vibration signal for analysis is the most common method for diagnosing gas valve faults. [PERSON] et al. [2] used basis pursuit to extract the principal components of the vibration signal and employed waveform matching to extract the signal features. Finally, he applied support vector machines to identify the valve failure mode. [PERSON] et al. [3] transformed the vibration signal of the valve into a high-dimensional vector space and defined the metric in this space. Finally, the distance between the actual state of the compressor and the reference state was calculated to determine whether a failure had occurred. [PERSON] et al. [4] used the vibration signal of the valve of the reciprocating compressor to the experiment. Firstly, he performed wavelet packet decomposition to extract features. Next, he applied principal component analysis to reduce the dimensionality of the obtained feature and then input the BP neural network for diagnosis. The weight optimization of the network used a combination of particle swarm optimization and the genetic algorithm, and the classification accuracy reached 100%. [PERSON] et al. [5] employed the symbolic dynamics and complex correlation measure to extract the features of vibration data as input to two random forest algorithms (ensemble subspace k nearest neighbor and ensemble bagged tree). The experimental results achieved a greater than 93% classification accuracy. 2. Thermal performance method: The thermal performance parameters of reciprocating compressors mainly include the component temperature, lubricating oil temperature, exhaust volume during operation, exhaust pressure, cylinder pressure, etc. [PERSON] [6] established a zero-dimensional numerical model of a single-stage reciprocating natural gas compressor. Next, he set a hole on the valve plate to simulate valve leakage and monitored the changes in temperature, pressure, and mass flow parameters at the inlet and outlet of the valve to diagnose valve faults. The model was verified by comparison with the existing experimental data. Finally, he proposed that the valve could be diagnosed by detecting the temperature change of the valve to diagnose whether the valve was faulty. [PERSON] [7] established a mathematical model of the working cycle of a reciprocating compressor and applied software to simulate the dynamic pressure curve of the compressor under the condition of air volume adjustment. Then, he added the fault influences parameters to improve the mathematical model. This simulated the leakage fault of the suction valve and the spring stiffness failure and the cylinder pressure data under failure was obtained. Finally, based on the principal component analysis method, he classified the valve fault status. However, if the method based on the above parameters is used in the early stage of the failure, these thermal performance parameters do not change significantly, and the failure cannot be predicted well. Therefore, professional technicians are required to monitor relevant parameters and it is difficult to achieve real-time diagnosis. Besides, measuring parameters require many sensors and the cost is relatively high. 3.