报告详情
Deep Learning Data-Driven Thresholding Models Incorporating Box-Cox Transforms
编号：66
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更新：2025-06-20 16:36:25 浏览：31次
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摘要
Abstracts—Aiming at the problems of high false alarm rates and poor adaptability of the traditional monitoring threshold establishment method based on empirical formulae, this paper proposes a data-driven threshold construction method (DL-DDBC) that integrates Box-Cox transform (B-CT) and deep learning. The method firstly constructs a steady state operating condition data set based on the target rotational speed screening and adopts the Savitzky-Golay filter (S-Golay filter) to achieve signal noise reduction. Then, the target benchmark data after noise reduction is tested for normality, and the feature thresholds are directly extracted from the data that meets the normal distribution, while the non-normal data is firstly corrected by B-CT, and the inverse B-CT is performed after extracting the eigenvalues of the corrected data; after that, a deep learning model is constructed to extract the benchmark eigenvalues of the data that characterize the health state of the equipment; finally, the threshold boundary is constructed by applying the improved threshold boundary generating algorithm. By the data analysis process designed in this paper, experiments are conducted with the gas turbine acoustic pressure signal as the research object, and the results show that the constructed threshold intervals have significant adaptability to the working conditions and anti-noise ability, and provide reliable quantitative evidence for real-time monitoring of gas turbine operation status.
关键词
gas turbine, anomaly analysis, deep learning, thresholding, S-Golay filter, B-CT, DL-DDBC.
报告人
婧怡 胡
博士研究生 哈尔滨工程大学稿件作者
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重要日期
-
会议日期
08月01日
2025
至08月04日
2025
-
06月23日 2025
初稿截稿日期
主办单位
中国机械工程学会设备智能运维分会
承办单位
新疆大学
联系方式
- Mrs. Yanruqiang
- ya******@xjtu.edu.cn
历届会议
-
2025年08月01日 中国 wulumuqi
2025 International Conference on Equipment Intelligent Operation and Maintenance -
2023年09月21日 中国 Hefei
第一届（国际）设备智能运维大会
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