Reciprocating compressors are widely used in industry for various purposes and faults occurring in them can degrade performance, consume additional energy, and even cause severe damage to the machine. This paper will develop an automated approach to condition classification of a reciprocating compressor based on vibration measurements. Both the time domain and frequency domain techniques have been applied to the vibration signals and a large number of candidate features have been obtained based on previous studies. A subset selection method has then been used to configure a probabilistic neural network (PNN), with high computational efficiency, for effective fault classifications. The results show that a 95.50% correct classification between four different faulty cases is the best result when using a subset of frequency feature, whereas a 93.05% rate is the best for the subset from the time domain.
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