A Tensor Train based Multi-channel Fault Signal De-noising Method for Wind Turbine Generators

Keren Li*, Wenqiang Zhang, Dandan Xiao, Peng Hou, Mohamed L. Shaltout, Xuerui Mao

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

With the continuous development of sensors and information collection related technologies, there are more and more types of data, and the amount of data of the same type is getting larger and larger. For mechanical components such as fan rotating gears, the use of a single sensor for fault diagnosis has limitations, and it is impossible to fully and simultaneously perform auxiliary fault diagnosis on gears, bearings and other components on each shaft. However, the use of multi-channel sensors can solve this problem well, but in actual engineering applications, there is noise interference when multi-channel sensors perform joint fault diagnosis, and it is impossible to accurately extract the fault characteristics of rotating machinery. This paper proposes a feature extraction method based on tensor train fused signal (TTFS) as a means of addressing noise interference in rotating machinery signals within wind farms. Initially, our focus is on data collection, during which we analyse data in tensor format across three dimensions: time, frequency, and channel. In order to emphasise the use of tensor train decomposition, we introduce a tensor reconstruction method with adaptive filter truncation. Subsequently, the continuous wavelet transform (CWT) method is employed to establish tensor data representation across these dimensions. Subsequently, the proposed adaptive filtering truncation tensor reconstruction method is employed to reconstruct the tensor by combining the aforementioned matrices. Finally, the continuous wavelet inverse transform is applied to the reconstructed tensor in order to obtain time-domain signals from different channels. The efficacy of the proposed method is demonstrated through experimental signals, which illustrate the advantages of the method and the improved convergence speed of the objective function.

源语言英语
主期刊名Proceedings - 2024 IEEE 4th International Conference on Digital Twins and Parallel Intelligence, DTPI 2024
出版商Institute of Electrical and Electronics Engineers Inc.
138-143
页数6
ISBN(电子版)9798350349252
DOI
出版状态已出版 - 2024
活动4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024 - Wuhan, 中国
期限: 18 10月 202420 10月 2024

出版系列

姓名Proceedings - 2024 IEEE 4th International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

会议

会议4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024
国家/地区中国
Wuhan
时期18/10/2420/10/24

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引用此

Li, K., Zhang, W., Xiao, D., Hou, P., Shaltout, M. L., & Mao, X. (2024). A Tensor Train based Multi-channel Fault Signal De-noising Method for Wind Turbine Generators. 在 Proceedings - 2024 IEEE 4th International Conference on Digital Twins and Parallel Intelligence, DTPI 2024 (页码 138-143). (Proceedings - 2024 IEEE 4th International Conference on Digital Twins and Parallel Intelligence, DTPI 2024). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DTPI61353.2024.10778726