Abstract
The valve is one of the important parts of the reciprocating compressor, which directly affects the thermodynamic process and reliability of the compressor. In this paper, acoustic emission (AE) technology is used to predict the dynamic characteristics of valves. The AE signal of the compressor valve is analyzed based on the deep learning method, and the mapping relation between the AE signal and the dynamic characteristics of the valve is obtained. The results show that the prediction accuracy of the models trained by Long Short-Term Memory (LSTM) artificial neural network and Convolutional Neural Network (CNN) is 97% and 95%, respectively, which can accurately predict the dynamic characteristics of the valve. Although the prediction results of CNN are slightly lower than that of LSTM network, the calculation speed of CNN is relatively faster.
Highlights
Reciprocating compressors, widely used for gas compression, play a crucial role in industry, including oil refineries, chemical plants, natural gas processing and delivery plants
The Long ShortTerm Memory (LSTM) network and Convolutional Neural Network (CNN) are used to map the relation between the acoustic emission (AE) signal and dynamic characteristics of the valve
The mapping relationship between the valve dynamic characteristics and AE signal is analyzed by using CNN [11] and LSTM network [12]
Summary
Reciprocating compressors, widely used for gas compression, play a crucial role in industry, including oil refineries, chemical plants, natural gas processing and delivery plants. Based on operating characteristics of reciprocating compressor and fault characteristics of the valves, the monitoring signals and fault diagnosis method selected by different researchers are different [6]. Based on the deep learning method, this paper analyzes the AE signal of the reciprocating compressor valve and proposes a fault diagnosis method. The LSTM network and CNN are used to map the relation between the AE signal and dynamic characteristics of the valve. The network model is used to analyze the delay closing characteristic of the valve, which provides theoretical and experimental basis for the fault diagnosis of reciprocating compressors with AE technology. The mapping relationship between the valve dynamic characteristics and AE signal is analyzed by using CNN [11] and LSTM network [12]. Split the data into the training set and the test set (Train 90%, Test 10%)
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