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Issue:ISSN 1006-5539
          CN 51-1183/TE


Your Position :Home->Past Journals Catalog->2020 Vol.6

Failure Rate Analysis and Prediction for Pneumatic Value in Large LNG Plant
Author of the article:Yang Ye, Zhang Jinlong, He Jingyi, Yang Xuan, Li Die, Deng Xiangjun
Author's Workplace:Hubei Xinjie LNG Project, Jianghan Oil Production Factory, Sinopec Jianghan Oilfield Branch Company, Huanggang, Hubei, 438011, China
Key Words:GM(1,1); Control valve; Fault; Gray sytem; LNG plant

This study aims to explore the development, and changes over time, of control valve failure rate in LNG plant, predict the frequency of repair and damage to key components, and guide the routine maintenance, purchase and inventory of spare parts. Data of control valve failure from a development project between 2014 to 2019 were collected to widen the issue analysis, and the Gray system mean GM(1,1) model was used to characterize the relevant problems. The study shows that :1) GM(1,1) model is an exponential fitting model based on accumulation generation and least square method. It is versatile and can be used with poor information and small samplesituation to get more accurate prediction results under the premise of limited time series data; 2)The failure prediction in 2019 obtained by this modeling is relatively reliable. The relative error of simulated positioner average maintenance frequency is only 3.67%. However, with longer time frame, the model prediction becomes less meaningful; 3) The next step is to use DGM model or SDGM model to comprehensively improve the accuracy of the original mean GM(1,1) model; 4) Although the control loop driving the control valve is simple; comparing valve opening ratios during shutdowns alone will not provide timely detection of overall control valve issues. In addition, this method has limited prediction time frame, so a rigorous and yet simple valve testing program needs to be developed, and relevant data could be adapted to GM(1,1) modeling. This study makes use of simple mathematical algorithm to predict, to a large extent, possible abnormal production scenarios in future operation, to enhance operation uptime at a reliable cost.

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