Presentation Details
Deep Learning Data-Driven Thresholding Models Incorporating Box-Cox Transforms
ID：66
View Protection：ATTENDEE
Updated Time：2025-06-20 16:36:25 Hits：330
Oral Presentation
Start Time：Pending（Asia/Shanghai）
Duration：Pending
Session：No Session »
No files
Abstract
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.
Keywords
gas turbine, anomaly analysis, deep learning, thresholding, S-Golay filter, B-CT, DL-DDBC.
Speaker
婧怡 胡
博士研究生 哈尔滨工程大学Submission Author
All Comments
Important Date
-
Conference Date
Aug 01
2025
toAug 04
2025
-
Aug 20 2025
Draft paper submission deadline
Sponsored By
中国机械工程学会设备智能运维分会
Organized By
新疆大学
Contact Information
- Mr. Yanruqiang
- ya******@xjtu.edu.cn
Previous Conferences
-
2025-08-01 China wulumuqi
2025 International Conference on Equipment Intelligent Operation and Maintenance -
2023-09-21 China Hefei
2023 International Conference on Equipment Intelligent Operation and Maintenance
Submit Comment