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http://dx.doi.org/10.18770/KEPCO.2018.04.02.101

Kernel Regression Model based Gas Turbine Rotor Vibration Signal Abnormal State Analysis  

Kim, Yeonwhan (KEPCO Research Institute, Korea Electric Power Corporation)
Kim, Donghwan (KEPCO Research Institute, Korea Electric Power Corporation)
Park, SunHwi (KEPCO Research Institute, Korea Electric Power Corporation)
Publication Information
KEPCO Journal on Electric Power and Energy / v.4, no.2, 2018 , pp. 101-105 More about this Journal
Abstract
In this paper, the kernel regression model is applied for the case study of gas turbine abnormal state analysis. In addition to vibration analysis at the remote site, the kernel regression model technique can is useful for analyzing abnormal state of rotor vibration signals of gas turbine in power plant. In monitoring based on data-driven techniques correlated measurements, the fault free training data of shaft vibration obtained during normal operations of gas turbine are used to develop a empirical model based on auto-associative kernel regression. This data-driven model can be used to predict virtual measurements, which are compared with real-time data, generating residuals. Any faults in the system may cause statistically abnormal changes in these residuals and could be detected. As the result, the kernel regression model provides information that can distinguish anomalies such as sensor failure in a shaft vibration signal.
Keywords
Monitoring; Rotor Vibration; Auto-Associative Kernel Regression; Correlation; Data-Driven Prediction Model; Residual; Anomaly;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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