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An exploratory study of underwater bolted connection looseness detection using percussion and a shallow machine learning algorithm

利用叩击和浅层机器学**算法进行水下螺栓连接件松动检测的探索性研究

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Abstract

As the looseness of underwater bolted connections may cause catastrophic consequences, their regular inspection is vital. This paper proposes an exploratory approach to detecting the looseness condition of underwater bolted connections by percussion. The sound produced by tap** a bolted connection will alter when the preload on the connection reduces. Using the power spectrum density for feature selection, the proposed approach employs the frequency feature change of impact-induced sounds and implements the KNN (K-nearest neighbors) algorithm, a shallow machine learning method, to identify the corresponding looseness status. Experiments demonstrate effective performances of the proposed method.

摘要

水下螺栓连接件松动可能导致灾难性后果, 因此, 对其进行定期检测至关重要. 本文探索性地提出了一种通过叩击检测水下螺栓连接件松动情况的方法. 当螺栓连接件预紧力降低时, 通过敲击其产生的声音会发生相应变化. 该方法利用功率谱密度进行特征选择, 根据叩击引起的声音频率特征变化, 采用浅层机器学**方法K**邻算法来识别相应的松动状态. 实验结果证明了该方法的有效性.

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Acknowledgements

This work was supported by Texas Commission on Environmental Quality through Subsea Systems Institute Award (Grant No. 582-15-57593). This project was paid for [in part] with federal funding from the Department of the Treasury through the State of Texas under the Resources and Ecosystems Sustainability, Tourist Opportunities, and Revived Economies of the Gulf Coast States Act of 2012 (RESTORE Act).

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Authors and Affiliations

  1. Department of Mechanical Engineering, University of Houston, Houston, TX, 77204, USA

    Sihong He  (何思宏), Ji’an Chen  (陈吉安), Zheng Chen  (陈峥) & Gangbing Song  (宋钢兵)

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  1. Sihong He  (何思宏)
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  2. Ji’an Chen  (陈吉安)
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  3. Zheng Chen  (陈峥)
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  4. Gangbing Song  (宋钢兵)
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Contributions

Gangbing Song acquired the funding to support this research and formed the basic concept of this work. Sihong He accomplished the formal analysis, investigation and methodology development, and collected the data. The work was supervised by Gangbing Song and Zheng Chen and validated by Sihong He and Ji’an Chen. The manuscript was drafted by Gangbing Song and Sihong He, and furthur reviewed and polished by Gangbing Song, Sihong He and Zheng Chen.

Corresponding author

Correspondence to Gangbing Song  (宋钢兵).

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He, S., Chen, J., Chen, Z. et al. An exploratory study of underwater bolted connection looseness detection using percussion and a shallow machine learning algorithm. Acta Mech. Sin. 39, 722360 (2023). https://doi.org/10.1007/s10409-023-22360-x

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