Prognostic Modeling of Valve Degradation within Power Stations

Mark J. McGhee, Grant Galloway, Victoria M. Catterson, Blair Brown, and Emma Harrison
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Full Paper
phmc_14_009.pdf2.23 MBSeptember 16, 2014 - 9:19am

Within the field of power generation, aging assets and a desire for improved maintenance decision-making tools have led to growing interest in asset prognostics. Valve failures can account for 7% or more of mechanical failures, and since a conventional power station will contain many hundreds of valves, this represents a significant asset base. This paper presents a prognostic approach for estimating the remaining useful life (RUL) of valves experiencing degradation, utilizing a similarity-based method. Case study data is generated through simulation of valves within a 400MW Combined Cycle Gas Turbine power station.

High fidelity industrial simulators are often produced for operator training, to allow personnel to experience fault procedures and take corrective action in a safe, simulation environment, without endangering staff or equipment. This work repurposes such a high fidelity simulator to generate the type of condition monitoring data which would be produced in the presence of a fault. A first principles model of valve degradation was used to generate multiple run-to-failure events, at different degradation rates. The associated parameter data was collected to generate a library of failure cases. This set of cases was partitioned into training and test sets for prognostic modeling and the similarity based prognostic technique applied to calculate RUL. Results are presented of the technique’s accuracy, and conclusions are drawn about the applicability of the technique to this domain.

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Submission Keywords: 
Data Driven
Valve Degradation
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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