Applications of machine learning to reciprocating compressor fault diagnosis: a review

Operating condition detection and fault diagnosis are very important for reliable operation of reciprocating compressors. Machine learning is one of the most powerful tools in this field. However, there are very few comprehensive reviews which summarize the current research of machine learning in monitoring reciprocating compressor operating condition and fault diagnosis. In this paper, the recent application of machine learning techniques in reciprocating compressor fault diagnosis is reviewed. The advantages and challenges in the detection process, based on three main monitoring parameters in practical applications, are discussed. Future research direction and development are proposed

Observations of changes in acoustic emission parameters for varying corrosion defect in reciprocating compressor valves

Acoustic Emission (AE) technology is probably one of the most recent entries in the field of machinery condition monitoring. This paper investigates the application of AE parameters for valve faults detection in reciprocating compressor. The defective valves were designed by emulating the actual valve corrosion with varying sizes such that defects could be applied onto the reciprocating compressor. A set of experiments was performed to acquire the AE signal. The primary source of AE signal was verified using waveform analysis. The AE parameters versus different operational and valve condition were illustrated individually. In addition, the significance of the change and sensitivity of AE parameters versus different experimental conditions was verified using MANOVA and coefficient of variance analysis. It is concluded that the AE signal parameters can be used to detect the valve faults in the reciprocating compressor with the consideration of the variation in the AE parameters sensitivity