Hyperparameter Tuning of OC-SVM for Industrial Gas Turbine Anomaly Detection

Hyun-Su Kang, Yun-Seok Choi, Jun-Sang Yu, Sung-Wook Jin, Jung-Min Lee and Youn-Jea Kim ()
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Hyun-Su Kang: Graduate School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
Yun-Seok Choi: Graduate School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
Jun-Sang Yu: Data Analytics Team, Doosan Enerbility, Seongnam 13557, Republic of Korea
Sung-Wook Jin: Data Analytics Team, Doosan Enerbility, Seongnam 13557, Republic of Korea
Jung-Min Lee: Data Analytics Team, Doosan Enerbility, Seongnam 13557, Republic of Korea
Youn-Jea Kim: School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea

Energies, 2022, vol. 15, issue 22, 1-12

Abstract: Gas turbine failure diagnosis is performed in this work based on seven types of tag data consisting of a total of 7976 data. The data consist of about 7000 normal data and less than 500 abnormal data. While normal data are easy to extract, failure data are difficult to extract. So, this study mainly is composed of normal data and a one-class support vector machine (OC-SVM) is used here, which has an advantage in classification accuracy performance. To advance the classification performance, four hyperparameter tuning (manual search, grid search, random search, Bayesian optimization) methods are applied. To analyze the performance of each technique, four evaluation indicators (accuracy, precision, recall, and F-1 score) are used. As a result, about 54.3% of the initial failure diagnosis performance is improved up to 64.88% through the advanced process in terms of accuracy.

Keywords: gas turbine; anomaly detection; OC-SVM; machine learning; AI; PHM (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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