Fault diagnosis of reciprocating compressor based on multi-scale permutation entropy

Abstract

Reciprocating compressor is a kind of power machinery widely used in the industrial field, in order to achieve the condition monitoring of reciprocating compressor and fault diagnosis of key components, the reciprocating compressor vibration signal acquisition experimental platform is designed, and the mechanical performance of the compressor is monitored by means of vibration detection and signal analysis. In addition, a fault feature extraction method integrating time domain, frequency domain and entropy value is proposed, the fault feature extraction of the processed vibration signal is carried out, the extracted fault feature is used as input, and the compressor fault diagnosis is carried out by using the limit learning machine algorithm, and the results show that the method can better diagnose and identify the faults of different parts of the reciprocating compressor.