Statistical Classification Approach to Valve Condition Monitoring Using Pressure Features

Abstract

This paper provides a novel health monitoring classification algorithm to estimate the level of valve leakage in reciprocating compressors. It is expected that the level of leakage will change the shape of the pressure-volume diagram. This method constructs a feature space using the polytropic exponent during the expansion and compression phases as well as the discharge and suction valve loss power. These features are extracted by measuring in-cylinder pressure, discharge pressure, suction pressure, and the crank angle. Linear and quadratic discriminant classifiers are used as machine learning approaches to classify the valve health of the compressor. This method is implemented on a single-stage double-acting industrial gas compressor operating on air. Faults are seeded by precisely machining valve poppets to simulate common valve leakage. The approach shows a high classification accuracy in determining the degree of leakage and shows promise for future work in prognostics.