A Statistical Classification Approach to Valve Condition Monitoring Using Pressure Features

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Published Oct 28, 2022
Jacob Chesnes Jason Kolodziej Daniel Nelson

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.

How to Cite

Chesnes, J., Kolodziej, J., & Nelson, D. (2022). A Statistical Classification Approach to Valve Condition Monitoring Using Pressure Features. Annual Conference of the PHM Society, 14(1). https://doi.org/10.36001/phmconf.2022.v14i1.3382
Abstract 49 | PDF Downloads 76

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Keywords

Compressor Valve, gaussian classification, valve loss power, polytropic exponent, PV diagram

Section
Technical Papers