Online Monitoring of Reciprocating Compressor Health Conditions

Due to the non-linear and non-smooth characteristics of reciprocating compressor vibration signals and the influence of environmental noise, the current online feature extraction methods are not suitable for direct fault diagnosis of reciprocating compressors. Hence, there is an urgent need to find out the characteristic parameters with low noise sensitivity for reciprocating compressor fault monitoring. In this paper, multi-scale fuzzy entropy is used to analyze the vibration signal at the main shaft of the reciprocating compressor motor, and the multi-scale fuzzy entropy is standardized to determine the operating status according to its change range. The field application of the compressor enables timely detection of wear faults at the main bearing of the motor, which is more stable and efficient than the traditional feature extraction method and can detect early faults.