A Modified Time Reversal Method for Guided Wave Based Bolt Loosening Monitoring in a Lap Joint

Xu, Chao; Wu, Guannan; Du, Fei; Zhu, Weidong; Mahdavi, Seyed Hossein

doi:10.1007/s10921-019-0626-1

Cited by 21 publications

(15 citation statements)

References 30 publications

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“…Xu et al and Jiang et al collected ultrasonic signals based on the time-reversal method. Bolt loosening was detected using the relationship between the maximum amplitude of the signal and the bolt preload force [12] [13]. A virtual time reversal method for damage localization was proposed by Kannusamy et al [14].…”

Section: Introductionmentioning

confidence: 99%

A nonlinear Lamb wave-based tight contact stage identification and pretightening state quantitative monitoring method for bolts

Tian

Wang

Yang

et al. 2023

Preprint

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Bolt connections are subjected to severe service conditions, such as cyclic loading and mechanical shock, leading to loosening failure. Commonly, the degradation of the bolt pretightening state is a multistage process, consisting of the tight contact stage (TCS) and significant loosening stage. Therefore, utilizing a single model to monitor the pretightening state in the full degradation stage is difficult. Here, a method based on nonlinear Lamb waves to identify the TCS of bolts and quantitatively monitor the pretightening state to bolt loosening is proposed. In the proposed method, phase reversal technology is first adopted to enhance the sensitivity and reduce the calculation errors of nonlinear damage indexes for bolt loosening in the TCS, and then the phase reversal relative nonlinear coefficient (PRC) is constructed. This indicator overcomes the disadvantage that linear indicators are insensitive to early loosening and realizes the identification of critical points between the TCS and the significant loosening stage, which provides a prerequisite for constructing a staged loosening monitoring model. After the TCS is determined, a quantitative monitoring model for loosening, which fuses seven nonlinear damage indexes, is established based on canonical correlation forests to evaluate the pretightening state. To verify the effectiveness of the method, an experimental study of bolts is carried out, the lamb signals under different loosening states are measured, and the monitoring effects of different indicators are compared and analyzed. The comparison results show that the proposed method has higher accuracy than conventional approaches.

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Section: Introductionmentioning

confidence: 99%

A nonlinear Lamb wave-based tight contact stage identification and pretightening state quantitative monitoring method for bolts

Tian

Wang

Yang

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Experimental results show that there is an approximately linear relationship between the signal peak value and pretightening torque of the bolt under a saturation problem, which can be used to monitor bolt loosening. Xu et al [ 14 ] proposed an improved time reversal method to monitor bolt loosening, which reconstructs the phase and amplitude of the signal. If there is a phase shift and amplitude difference between the signal generated by the structure in the healthy state and the reconstructed signal, it indicates that the structure is damaged.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Detection of Bolt Loosening Using YOLOv5

Sun

Dong

et al. 2022

Sensors

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Bolted connections have been widely applied in engineering structures, loosening will happen when bolted connections are subjected to continuous cyclic load, and a significant rotation between the nut and the bolt can be observed. Combining deep learning with machine vision, a bolt loosening detection method based on the fifth version of You Only Look Once (YOLOv5) is proposed, and the rotation of the nut is identified to detect the bolt loosening. Two different circular markers are added to the bolt and the nut separately, and then YOLOv5 is used to identify the circular markers, and the rotation angle of the nut against the bolt is calculated according to the center coordinate of each predicted box. A bolted connection structure is adopted to illustrate the effectiveness of the method. First, 200 images containing bolts and circular markers are collected to make the dataset, which is divided into a training set, verification set and test set. Second, YOLOv5 is used to train the model; the precision rate and recall rate are respectively 99.8% and 100%. Finally, the robustness of the proposed method in different shooting environments is verified by changing the shooting distance, shooting angle and light condition. When using this method to detect the bolt loosening angle, the minimum identifiable angle is 1°, and the maximum detection error is 5.91% when the camera is tilted 45°. The experimental results show that the proposed method can detect the loosening angle of the bolted connection with high accuracy; especially, the tiny angle of bolt loosening can be identified. Even under some difficult shooting conditions, the method still works. The early stage of bolt loosening can be detected by measuring the rotation angle of the nut against the bolt.

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“…This method obtains the focused signal peak amplitude during ultrasonic wave propagation through the bolted connection surface, which reveals the value of the applied preload. Based on this technique, Xu et al [ 6 ] optimized the gathering approach of the ultrasonic signal to improve the accuracy and to expand the range of sensitivity. Zhao et al [ 7 ] applied this method on looseness testing of bolted connections on a wood structure, and put forward the loss index of the preload of a bolt to describe the looseness of a bolted connection in wood structures.…”

Section: Introductionmentioning

confidence: 99%

Detection Method for Bolted Connection Looseness at Small Angles of Timber Structures based on Deep Learning

Liu

Chen

et al. 2021

Sensors

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Bolted connections are widely used in timber structures. Bolt looseness is one of the most important factors leading to structural failure. At present, most of the detection methods for bolt looseness do not achieve a good balance between cost and accuracy. In this paper, the detection method of small angle of bolt loosening in a timber structure is studied using deep learning and machine vision technology. Firstly, three schemes are designed, and the recognition targets are the nut’s own specification number, rectangular mark, and circular mark, respectively. The Single Shot MultiBox Detector (SSD) algorithm is adopted to train the image datasets. The scheme with the smallest identification angle error is the one identifying round objects, of which the identification angle error is 0.38°. Then, the identification accuracy was further improved, and the minimum recognition angle reached 1°. Finally, the looseness in a four-bolted connection and an eight-bolted connection are tested, confirming the feasibility of this method when applied on multi-bolted connection, and realizing a low operating costing and high accuracy.

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A Modified Time Reversal Method for Guided Wave Based Bolt Loosening Monitoring in a Lap Joint

Cited by 21 publications

References 30 publications

A nonlinear Lamb wave-based tight contact stage identification and pretightening state quantitative monitoring method for bolts

A nonlinear Lamb wave-based tight contact stage identification and pretightening state quantitative monitoring method for bolts

Vision-Based Detection of Bolt Loosening Using YOLOv5

Detection Method for Bolted Connection Looseness at Small Angles of Timber Structures based on Deep Learning

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