Fault Diagnosis of Reciprocating Compressor Valve Based on Triplet Siamese Neural Network

Abstract

A fault diagnosis method for reciprocating compressor valves suitable for variable operating conditions is presented in this paper. Firstly, a test bench is independently constructed to simulate fault scenarios under diverse operating conditions and with various faults. The two types of p-V diagrams are gathered, and the improved logarithmic p-V diagram acquisition method is used for logarithmic transformation to obtain the multi-conditional logarithmic p-V diagram dataset and the fault logarithmic p-V diagram dataset. Subsequently, to predict the fault-free logarithmic p-V diagram under different operating conditions, a BP neural network is trained with the multi-condition logarithmic p-V diagram dataset. Next, the fault sequence is derived by subtracting the fault logarithmic p-V diagram from the fault-free logarithmic p-V diagram acquired under the same operating condition. Ultimately, the feature extraction of the fault sequence and the fault classification are accomplished by the employment of a triplet Siamese neural network (SNN). The results indicate that the fault classification accuracy of the method presented in this paper can attain 100%, which confirms that differential processing on the logarithmic p-V diagram is effective for fault feature preprocessing. This study not only improves the accuracy and efficiency of valve fault diagnosis in reciprocating compressors but also provides technical support for maintenance and fault prevention.