If structure can exclaim: a novel robotic-assisted percussion method for spatial bolt-ball joint looseness detection
If structure can exclaim: a novel robotic-assisted percussion method for spatial bolt-ball joint looseness detection
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DOI：
10.1177/1475921720923147
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发表时间：
2020-06
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影响因子：
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通讯作者：
Furui Wang;Aryan Mobiny;Hien Van Nguyen;G. Song
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文献类型：
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作者：
Furui Wang;Aryan Mobiny;Hien Van Nguyen;G. Song
与空间结构的巨大建造成比例的是与结构损伤（例如，连接松动）相关的灾难的出现，从而造成人身伤害和财产损失。因此，必须检测空间螺栓松动。现有的空间螺栓松动检测方法大多采用接触式测量，在某些情况下可能不实用。因此，受基于声音的人类诊断方法的启发，我们在本文中开发了一种新的敲击方法，使用梅尔频率倒谱系数和记忆增强神经网络。与目前的研究相比，本文的主要贡献是首次以比现有方法更高的精度检测多个螺栓松动。特别是，对于通过相似关节获得的新数据，记忆增强神经网络可以帮助避免低效的重新学习并吸收新数据，从而仅用少量数据样本就可以提供准确的预测，这有效地提高了检测的鲁棒性。此外，用机械臂代替人工操作实现了冲击，初步探索了在真实的工业中实现自动化应用的潜力。最后，实验结果表明，所提出的方法的有效性，这可以指导未来的网络物理系统的结构健康检测。
In proportion to the immense construction of spatial structures is the emergence of catastrophes related to structural damages (e.g. loose connections), thus rendering personal injury and property loss. It is therefore essential to detect spatial bolt looseness. Current methods for detecting spatial bolt looseness mostly focus on contact-type measurement, which may not be practical in some cases. Thus, inspired by the sound-based human diagnostic approach, we develop a novel percussion method using the Mel-frequency cepstral coefficient and the memory-augmented neural network in this article. In comparison with current investigations, the main contribution of this article is the detection of multi-bolt looseness for the first time with higher accuracy than prior methods. In particular, in terms of new data obtained via similar joints, the memory-augmented neural network can help avoid inefficient relearn and assimilate new data to provide accurate prediction with only a few data samples, which effectively improves the robustness of detection. Furthermore, percussion was implemented with a robotic arm instead of manual operation, which preliminarily explores the potential of implementing automation applications in real industries. Finally, experimental results demonstrate the effectiveness of the proposed method, which can guide future development of cyber-physics systems for structural health detection.