Application of Minimum Entropy Deconvolution in Diagnosis of Reciprocating Compressor Faults Based on Airborne Acoustic Analysis

The airborne acoustic signals from reciprocating compressors (RC) exhibit impulsive periodic transient response and are modulated due to several reasons, including structural and acoustic resonance. The occurrence of faults like intercooler leakage, filter blockage and compound faults like combination of intercooler and discharge valve leakage can enhance the feature characteristics of the signal. As a result the randomized periodic impulse and the presence of non linearity due to valve fluttering can contribute to the series of harmonic components in the acquired signal. Thus common methods have limitation to identify the characteristic features from the signal submerged in high background noise. In this paper, a deconvolution technique named as minimum entropy deconvolution (MED) has been adopted to extract the features of the impulses filtering out the non-transient components from the signal and providing a filtered output that only contains the periodic and transient components of the signal. The filtered signals are then analysed by estimating the RMS and entropy values under various operating pressures with the presence of different faults. The analysis result from the entropy of the filtered signal performs adequate enough to diagnose the conditions of the reciprocating compressor and hence finds suitable application of the method in diagnosis of the compound fault using the airborne acoustic signal, making it a remote and cost-effective condition monitoring technique.