Application based comparison of statistical versus deep learning approaches to reciprocating compressor valve condition monitoring

Abstract

This paper presents a vibration-based condition monitoring approach for early assessment of valve wear in an industrial reciprocating compressor. Valve seat wear is a common fault mode that is caused by repeated impact and accelerated by chatter. Seeded faults consistent with valve seat wear are installed on the head-side discharge valves of a Dresser-Rand ESH-1 industrial reciprocating compressor. Due to the cyclostationary nature of these units a time-frequency analysis is employed where targeted crank angle positions can isolate externally mounted, non-invasive, vibration measurements. A region-of-interest (ROI) is then extracted from the time-frequency analysis and used to train a suitably sized convolutional neural network (CNN). The proposed deep learning method is then compared against a similarly trained discriminant classifier using the same ROIs where features are extracted using texture and shape image statistics. Both methods achieve > 90% success with the CNN classification strategy nearing a perfect result.