Abstract
Reciprocating compressors are important equipment in oil and gas industries which closely relate with the healthy development of the enterprise. It is essential to detect the valve fault because valve failures account for 60% in total failures. For this field, an artificial neural network (ANN) is widely used, but a complex network is not suitable for its low accuracy and easy overfitting. This paper proposes a fault diagnosis model of a reciprocating compressor valve based on a one-dimensional convolutional neural network (1DCNN). This method takes the differential pressure and differential temperature of each compressor stage as the input of 1DCNN, using the characteristics of the CNN to extract the features and finally using Softmax to classify the fault. In order to verify this method, it is compared with LM-BP, RBF, and BP neural networks. The results show that the fault recognition rate of 1DCNN reaches 100%, which proves the effectiveness and feasibility of the proposed method.
Highlights
Reciprocating compressor is one of the most widely used compressor technologies in today’s oil and gas industries
Due to its complicated structure and many vulnerable parts, once a failure cannot be detected and eliminated in time, it will bring huge losses to the enterprise [1,2,3,4,5,6,7]. e literature indicates that 60% of reciprocating compressor failures are valve failures, and the number of shutdowns caused by valve failures accounted for 36% and accounted for 50% of the total maintenance costs [8,9,10]. erefore, monitoring the failure of the reciprocating compressor valve can reduce the overall maintenance cost and improve the stability of the compressor operation
Liu et al [13] proposed an intelligent fault diagnosis method combining local mean decomposition (LMD) and stack noise reduction automatic encoder (SDAE) to classify faults of reciprocating compressor gas valves. e results show that the classification accuracy of this method can reach 92.72%
Summary
Reciprocating compressor is one of the most widely used compressor technologies in today’s oil and gas industries. Yang et al [16] used three sensors to collect vibration signals when the reciprocating compressor valve fails, and directly used as the input of the convolutional neural network to make full use of its characteristics that automatically extract characteristic signals, and carried out fault diagnosis and obtained higher fault recognition rate. A fault diagnosis model of a reciprocating compressor valve based on one-dimensional convolutional neural network is established, the features in the sample data are automatically extracted, and fault classification is performed by the Softmax function. E advantages of this method are (1) directly using the temperature signal and pressure signal can well achieve online fault diagnosis and improve the quality and efficiency of detection and (2) automatically extracting the fault characteristics of the reciprocating compressor valve through the model, without the need to manually extract the characteristics and selection, which improves the accuracy of fault diagnosis A fault diagnosis model of a reciprocating compressor valve based on one-dimensional convolutional neural network is established, the features in the sample data are automatically extracted, and fault classification is performed by the Softmax function. e advantages of this method are (1) directly using the temperature signal and pressure signal can well achieve online fault diagnosis and improve the quality and efficiency of detection and (2) automatically extracting the fault characteristics of the reciprocating compressor valve through the model, without the need to manually extract the characteristics and selection, which improves the accuracy of fault diagnosis
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