Flange bolted connection state recognition based on time series two dimensionalization and attention mechanism

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Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (9) : 181-188.

ZHANG Hong1,2, LIU Binbin1,2, LI Yunfei3

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Abstract

When the flange bolt connection is dynamically excited with vibration, it is difficult to identify the connection state of the flange bolt with the collected acoustic emission signal. In order to solve this problem, a signal recognition and classification method based on attention network is proposed, in which the AE signal is encoded into a two-dimensional image with piecewise aggregation approximation and Gramm angle field, and the image is used instead of one-dimensional signal as the basis for diagnosis. The attention mechanism is introduced into the residual neural network, for improving the ability of feature extraction and recognition of the image. A group convolution diagnosis model is designed to realize the high-precision recognition of the flange bolt connection state. Compared with the selected methods, the experimental results show that this method can effectively identify the connection state of bolts and has strong detail feature extraction ability and generalization performance.

Key words

Flange bolt / Dynamic excitation / Acoustic emission signal / Gramian angular field / Attention mechanism

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ZHANG Hong1,2, LIU Binbin1,2, LI Yunfei3. Flange bolted connection state recognition based on time series two dimensionalization and attention mechanism[J]. Journal of Vibration and Shock, 2022, 41(9): 181-188

References

[1] Wang Y Q, Wu J K, Liu H B, et al. Analysis of elastic interaction stiffness and its effect on bolt preloading[J]. International Journal of Mechanical Sciences, 2017, 130: 307-314.
[2] 王崴, 徐浩, 马跃, 等. 振动工况下螺栓连接自松弛机理研究[J]. 振动与冲击, 2014(22): 198-202.
WANG Wei, XU Hao, MA Yue, et al. Self-loosening mechanism of bolted joints under vibration [J]. Journal of Vibration and Shock, 2014(22): 198-202.
[3] Wang F, Ho S C M, Song G. Monitoring of early looseness of multi-bolt connection: a new entropy-based active sensing method without saturation[J]. Smart Materials and Structures, 2019, 28(10): 10LT01.
[4] 艾延廷, 刘成明, 王志, 等. 基于非线性阻尼识别的螺栓连接检测技术[J]. 振动与冲击, 2020, 39(9): 138-143,180.
AI Yan-ting, LIU Cheng-ming, WANG Zhi, et al. Technology for detecting bolted joints based on nonlinear damping identification [J]. Journal of Vibration and Shock, 2020, 39(9): 138-143,180.
[5] Shah J K , Mukherjee A . Monitoring and Imaging of Bolted Steel Plate Joints Using Ultrasonic Guided Waves[J]. Journal of Nondestructive Evaluation Diagnostics and Prognostics of Engineering Systems, 2020, 4(1):1-20.
[6] 杜飞, 张子涵, 徐超. 法兰螺栓松动的超声导波监测方法[J]. 压电与声光, 2019, 41(5): 679-684.
DU Fei, ZHANG Zi-han, XU Chao. Ultrasonic Guided Wave Monitoring Method for Flange Bolt Loosening [J]. Piezoelectrics & Acoustooptics, 2019, 41(5): 679-684.
[7] 潘勤学, 邵唱, 肖定国, 等. 基于形状因子的螺栓紧固力超声检测方法研究[J]. 兵工学报, 2019, 40(4): 880-888.
PAN Qin-xue, SHAO Chang, XIAO Ding-guo, et al. Study of Ultrasonic Measurement Method for Bolt Fastening Force Based on Shape Factor [J]. Acta Armamentarii, 2019, 40(4): 880-888.
[8] 孙朝明, 孙凯华, 孙鹏飞, 等. 超声法测量螺栓轴向力的测量准确度分析[J]. 振动与冲击, 2021, 40(8): 85-91.
SUN Chao-ming，SUN Kai-hua，SUN Peng-fei，et al. Analysis on measurement accuracy of bolt axial force by ultrasonic technique. Journal of Vibration and Shock, 2021, 40(8): 85-91.
[9] Kong Q, Zhu J, Ho S C M, et al. Tapping and listening: A new approach to bolt looseness monitoring[J]. Smart Materials and Structures, 2018, 27(7): 07LT02.
[10] Amerini F, Meo M. Structural health monitoring of bolted joints using linear and nonlinear acoustic/ultrasound methods[J]. Structural health monitoring, 2011, 10(6): 659-672.
[11] 王怡, 王宁, 卢萍, 等. 基于声发射原理的螺栓连接状态辨识方法研究[J]. 声学技术, 2010, 29(5): 453-456.
WANG Yi, WANG Ning, LU Ping, et al. The study of identifying the state of bolted joint structure based on acoustic emission principle [J]. Technical Acoustics, 2010, 29(5): 453-456.
[12] 张健奎, 王宁, 卢萍, 等. 辨识振动环境中两点螺栓连接状态的REE声发射指标[J]. 振动与冲击, 2013, 32(8): 179-182.
ZHANG Jian-kui, WANG Ning, LU Ping, et al. AE REE index to identify connecting state of a 2-bolt connected structure [J]. Journal of Vibration and Shock, 2013, 32(8): 179-182.
[13] Wang Z, Oates T. Imaging time-series to improve classification and imputation[J]. arXiv preprint arXiv:1506.00327, 2015.
[14] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]. 2016 IEEE conference on computer vision and pattern recognition. Las Vegas: IEEE ,2016.
[15] Mnih V, Heess N, Graves A. Recurrent models of visual attention[C]. Advances in neural information processing systems, models of visual attention[C]. Advances in neural information processing systems, 2014: 2204-2212.
[16] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]. Proceedings of the IEEE conference on computer vision and pattern recognition, 2018: 7132-7141.
[17] Miao R, Shen R, Zhang S, et al. A Review of Bolt Tightening Force Measurement and Loosening Detection[J]. Sensors, 2020, 20(11): 3165.
[18] Zhang Z, Liu M, Su Z, et al. Quantitative evaluation of residual torque of a loose bolt based on wave energy dissipation and vibro-acoustic modulation: A comparative study[J]. Journal of Sound and Vibration, 2016, 383: 156-170