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research-article

Estimating Recoverable Performance Degradation Rates and Optimizing Maintenance Scheduling

[+] Author and Article Information
Cody Allen

Solar Turbines Incorporated, San Diego, California, USA
allen_cody_w@solarturbines.com

Chad Holcomb

Solar Turbines Incorporated, San Diego, California, USA
holcomb_chad_m@solarturbines.com

Mauricio C. de Oliviera

Dept. of Mechanical and Aeronautical Engineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0411, USA
mauricio@ucsd.edu

1Corresponding author.

ASME doi:10.1115/1.4041004 History: Received June 23, 2018; Revised July 06, 2018

Abstract

Many of the components on a gas turbine are subject to fouling and degradation over time due to debris buildup. For example, axial compressors are susceptible to degradation as a result of debris buildup on compressor blades. Similarly, air cooled lube oil heat exchangers incur degradation as a result of debris buildup in the cooling air passageways. In this paper, we develop a method for estimating the degradation rate of a given gas turbine component that experiences recoverable degradation due to normal operation over time. We then establish an economic maintenance scheduling model which utilizes the derived rate and user input economic factors to provide a locally optimal maintenance schedule with minimized operator costs. The rate estimation method makes use of statistical methods combined with historical data to give an algorithm with which a performance loss rate can be extracted from noisy data measurements. The economic maintenance schedule is then derived by minimizing the cost model in user specified intervals and the final schedule results as a combination of the locally optimized schedules. The goal of the combination of algorithms is to maximize component output and efficiency, while minimizing maintenance costs. The rate estimation method is validated by simulation where the underlying noisy data measurements come from a known probability distribution. Then, an example schedule optimization is provided to validate the economic optimization model and show the efficacy of the combined methods.

Solar Turbines Incorporated
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