A Vision-Based Monitoring Method for the Looseness of High-Strength Bolt

Pan, Yue; Ma, Yunlong; Dong, Yiqing; Gu, Zhidong; Wang, Dalei

doi:10.1109/tim.2021.3101316

Cited by 22 publications

(11 citation statements)

References 30 publications

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“…Previous work demonstrated how accelerometers are a good fit for devices health assessment [34][35] [36], and the aforehand mentioned signal processing techniques, used isolated, are proven to extract useful information from accelerometer data. In particular, the author in [22] detected mechanical bearing fault using an FFT, while in [37] and [38] Convolutional Neural Network (CNN) are exploited to detect bolt-nut alignment. In our work, both FFT and deep learning methods are leveraged to further optimize the energy efficiency of the proposed solution.…”

Section: Related Workmentioning

confidence: 99%

Design and Performance Evaluation of an Ultralow-Power Smart IoT Device With Embedded TinyML for Asset Activity Monitoring

Giordano¹,

Baumann²,

Crabolu

et al. 2022

IEEE Trans. Instrum. Meas.

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Section: Related Workmentioning

confidence: 99%

Design and Performance Evaluation of an Ultralow-Power Smart IoT Device With Embedded TinyML for Asset Activity Monitoring

Giordano¹,

Baumann²,

Crabolu

et al. 2022

IEEE Trans. Instrum. Meas.

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“…The test results showed that the average bolt damage detection accuracy was 0.9503. Pan et al 14 proposed a vision‐based bolt monitoring system with an Internet of Things (IoT) device. The results showed that the proposed system was sensitive in identifying angle changes and could monitor bolt loosening.…”

Section: Introductionmentioning

confidence: 99%

An experimental study on the identification of the root bolts' state of wind turbine blades using blade sensors

Gao,

Qian,

et al. 2024

Wind Energy

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Bolt looseness may occur on wind turbine (WT) blades exposed to operational and environmental variability conditions, which sometimes can cause catastrophic consequences. Therefore, it is necessary to monitor the loosening state of WT blade root bolts. In order to solve this problem, this paper proposes a method to monitor the looseness of blade root bolts using the sensors installed on the WT blade. An experimental platform was first built by installing acceleration and strain sensors for monitoring bolt looseness. Through the physical experiment of blade root bolts' looseness, the response data of blade sensors is then obtained under different bolt looseness numbers and degrees. Afterwards, the sensor signal of the blade root bolts is analyzed in time domain, frequency domain, and time‐frequency domain, and the sensitivity features of various signals are extracted. So the eigenvalue category as the input of the state discrimination model was determined. The LightGBM (light gradient boosting machine) classification algorithm was applied to identify different bolt looseness states for the multi‐domain features. The impact of different combinations of sensor categories and quantities as the data source on the identification results is discussed, and a reference for the selection of sensors is provided. The proposed method can discriminate four bolt states at an accuracy of around 99.8% using 5‐fold cross‐validation.

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“…Ramana et al 19 improved the method in Cha et al 18 by using the Viola-Jones algorithm to automatically localize the bolt in the image. Recently, by discriminating the rotation angle of the nuts, deep learning-based (DL-based) methods [20][21][22][23] were proposed for identifying loose bolts. Nonetheless, vision-based articles all ignore a problem that, in the early stage of the bolted connection looseness, it may not cause visible changes in the rotation angle and position of the nut.…”

Section: Introductionmentioning

confidence: 99%

Underwater bolted flange looseness detection using percussion-induced sound and Feature-reduced Multi-ROCKET model

Chen

Zheng

Zhu

et al. 2023

Structural Health Monitoring

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Recently, in the field of structural health monitoring, the detection of bolted connection looseness through percussion-based method and machine learning technology has received much attention due to the advantages of removing the requirement of sensor installation and potential for automation. However, there are few such research which are performed in the underwater environment. The paper proposes a new method, Feature-reduced Multiple Random Convolution Kernel Transform (FM-ROCKET), to identify the looseness level of the underwater bolted connections based on the percussion-induced sound (audio signal). By integrating deep learning (DL) and shallow learning, the FM-ROCKET model uses the 1D convolutional layer (a DL method) to extract features from the percussion-induced audio signal and adopts the rigid classifier (linear classifier, a shallow learning method) to classify the features. Five different preload levels of the bolted flange are considered. A hammer is utilized to tap the flange surface and the continuous percussion-induced audio signal is collected by a smartphone in an underwater environment. After the audio signal segmentation, single-hit audio signals are fed into the FM-ROCKET model. To verify the effectiveness of the proposed method, three case studies are conducted on two flanges. In case study I, the proposed method slightly outperforms other DL-based methods under different training/test splitting ratios. In case studies II and III, the proposed method is far more effective than other DL-based methods on independent and different test sets. The results demonstrate the superiority of the FM-ROCKET model in the underwater detection of bolted flange looseness. To the best of our knowledge, this article is the first attempt to address the detection of bolted flange looseness in the underwater environment by combining percussion-based method, DL, and shallow learning.

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A Vision-Based Monitoring Method for the Looseness of High-Strength Bolt

Cited by 22 publications

References 30 publications

Design and Performance Evaluation of an Ultralow-Power Smart IoT Device With Embedded TinyML for Asset Activity Monitoring

Design and Performance Evaluation of an Ultralow-Power Smart IoT Device With Embedded TinyML for Asset Activity Monitoring

An experimental study on the identification of the root bolts' state of wind turbine blades using blade sensors

Underwater bolted flange looseness detection using percussion-induced sound and Feature-reduced Multi-ROCKET model

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