A Feature Extraction Method for Prognostic Health Assessment of Gas Compressor Valves

Author:

Chesnes Jacob J.1,Nelson Daniel A.2,Kolodziej Jason R.1

Affiliation:

1. Rochester Institute of Technology Department of Mechanical Engineering, , Rochester, NY 14623

2. Novity, Inc. Director of Advanced Development, , San Carlos, CA 94070

Abstract

Abstract This article presents features derived from the pressure–volume (PV) diagram that is useful in estimating different valve faults in reciprocating compressors with a strong potential of remaining useful life prediction. The PV diagram is expected to deviate depending on valve wear conditions. Common valve degradation scenarios are explored in this work (leakage, seat wear, and spring fatigue) and are located in the suction and discharge assemblies of a Dresser-Rand ESH-1 compressor commonly used in the petrochemical industry. The proposed method estimates well-understood physical phenomena, the polytropic exponent on the compression, and expansion phase as well as the discharge and suction valve loss power and uses them as features for a quadratic discriminant analysis. The features are created through in-cylinder pressure, suction pressure, discharge pressure, and crank angle measurements collected on a single-stage, dual-acting compressor operating on air with wear precisely machined and seeded into the poppets of the inlet and outlet valves. A very high classification accuracy is achieved in distinguishing the wear types, severity, and location with strong prognostic trends.

Publisher

ASME International

Reference12 articles.

1. On-line Monitoring of Reciprocating Compressors;Schirmer,2004

2. Fault Detection in Reciprocating Compressor Valves for Steady-State Load Conditions;Pichler,2011

3. Fault Detection in Reciprocating Compressor Valves Under Varying Load Conditions;Pichler;Mech. Syst. Signal Process.,2016

4. An Image-Based Pattern Recognition Approach to Condition Monitoring of Reciprocating Compressor Valves;Kolodziej;J. Vib. Control,2018

5. An Application Based Comparison of Statistical Versus Deep Learning Approaches to Reciprocating Compressor Valve Condition Monitoring;Chesnes,2021

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