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contributor authorChesnes, Jacob J.
contributor authorNelson, Daniel A.
contributor authorKolodziej, Jason R.
date accessioned2025-04-21T10:20:25Z
date available2025-04-21T10:20:25Z
date copyright6/7/2024 12:00:00 AM
date issued2024
identifier issn2572-3901
identifier othernde_7_4_041001.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4305976
description abstractThis 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.
publisherThe American Society of Mechanical Engineers (ASME)
titleA Feature Extraction Method for Prognostic Health Assessment of Gas Compressor Valves
typeJournal Paper
journal volume7
journal issue4
journal titleJournal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
identifier doi10.1115/1.4065546
journal fristpage41001-1
journal lastpage41001-9
page9
treeJournal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems:;2024:;volume( 007 ):;issue: 004
contenttypeFulltext


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