Abstract:  Aiming at the problem that the signal at the gas valve measuring point is the comprehensive response of multiple excitation sources, it is difficult to accurately evaluate the health state of the reciprocating compressor gas valve by only relying on a single signal. Therefore, a reciprocating compressor gas valve health evaluation method based on multi-source signal fusion was proposed. Firstly, taking a type of four-stage high-pressure reciprocating compressor as the research object, the thermal parameters and acoustic emission signals in different health states of the inlet gas valve and exhaust gas valve were obtained through fault simulation test. Then, the time-domain characteristics, frequeny-domain characteristics and thermal parameters of different signal sources were extracted, and the mean value of each characteristic parameter under the gas valve health state was used as the health benchmark to calculate the Mahalanobis distance (MD) between the gas valve samples with different health states and the health baseline. Based on the multi-source signal fusion theory, the Mahalanobis distance obtained from the multi-source signal was fused to reconstruct the sample. Finally, the gas valve health state evaluation model was constructed based on the decision tree to evaluate the health state of the gas valve. The research results show that the evaluation accuracy of each single signal source is respectively 70.6%, 87.2% and 85.2%, while the differentiation of different health states of samples reconstructed based on multi-source signal fusion is significantly improved. The gas valve health status evaluation model built based on decision tree can effectively identify the health status of the gas valve with an accuracy of 100%, and has better evaluation effect.
Key words: piston compressor; characteristic parameter extraction of gas valve; Mahalanobis distance (MD); health assessment method; decision tree; data acquisition system; fault simulation experiment; sample reconstruction