[1] Chen Hao, Zhu Yikai, Lei Bo, Weng Zhihai, et al. Sensor fault self-detection based on the mean shift method [J]. Journal of Southeast University (English Edition), 2024, 40 (2): 140-147. [doi:10.3969/j.issn.1003-7985.2024.02.004]

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Sensor fault self-detection based on the mean shift method()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

40

Issue:

2024 2

Page:

140-147

Research Field:

Civil Engineering

Publishing date:

2024-06-13

Info

Title:

Sensor fault self-detection based on the mean shift method

Author(s):

Chen Hao¹;  Zhu Yikai¹;  Lei Bo²;  Weng Zhihai³;  Xu Hongchang²;  Wan Huaping¹

¹College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
²China Construction Third Engineering Bureau Co., Ltd., Wuhan 430064, China
³Huzhou City Investment and Development Group Co., Ltd., Huzhou 313000, China

Keywords:

sensor fault detection;  mean shift;  sliding window;  machine learning

PACS:

TU317

DOI:

10.3969/j.issn.1003-7985.2024.02.004

Abstract:

To accurately identify sensor faults caused by complex environmental conditions and ensure that structural health monitoring systems correctly perceive the structural state, a self-detection method for sensor nodes based on mean shift and sliding window techniques was proposed. The self-detection method comprises two stages, i.e., fault prescreening and fault self-detection. During the fault prescreening stage, the method rapidly identifies potentially abnormal data using the quartile method combined with the sliding window technique, significantly improving the efficiency of the method. During the fault self-detection stage, the method employs the mean shift algorithm to perform adaptive clustering of the abnormal data, effectively detecting various faults. Data from the Canton Tower were used to test the effectiveness of the method by setting four types of sensor faults, i.e., offset, drift, gain, and stuck. Then, the proposed method was compared with extremely randomized trees, random forests, support vector data description, and one-class support vector machines. Results show that the proposed method can detect the four aforementioned faults with high accuracy and computational efficiency.

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Memo

Memo:

Biographies: Chen Hao(2000—), male, Ph.D. candidate; Wan Huaping(corresponding author), male, doctor, professor, hpwan@zju.edu.cn.
Foundation items: The National Key Research and Development Program of China(No. 2021YFF0501001), Zhejiang Provincial Natural Science Foundation(No. LR23E080003).
Citation: Chen Hao, Zhu Yikai, Lei Bo, et al. Sensor fault self-detection based on the mean shift method[J].Journal of Southeast University(English Edition), 2024, 40(2):140-147.DOI:10.3969/j.issn.1003-7985.2024.02.004.

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