Multi-bolt looseness state monitoring using the recursive analytic based active sensing technique

Jiang, Jian; Chen, Yixuan; Liang, Yabin

doi:10.1016/j.measurement.2022.110779

Cited by 23 publications

(17 citation statements)

References 35 publications

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“…In this case, we employ datasets from different flanges to respectively serve as training and test sets to study the method's robustness and its potential for practical application. As mentioned above, datasets (1,2,3,4) are captured from the flange A and datasets (5,6,7,8) are from the flange B. The trial settings are shown in Table 5, the performances of different methods are illustrated in Figure 11, and the confusion matrices are given in Figure 12.…”

Section: Resultsmentioning

confidence: 99%

“…Specifically, eight datasets are collected by two researchers in 1 week and operators first loosen the bolts before tightening them under each preload level in each dataset, which ensures that classes are independent of each other and datasets are independent of each other. Datasets (1, 2, 3, 4) are taken from the flange A and datasets (5,6,7,8) are taken from the flange B. Figure 6 exhibits single-hit audio signals under different datasets and preload levels.…”

Section: Methodsmentioning

confidence: 99%

“…In the past decades, benefitting from the rapid development in structural health monitoring (SHM), [5][6][7] researchers have been making contributions to the detection of bolted connection looseness in the air. [8][9][10] Notably, detection approaches based on the piezoelectric material 11 have been developed. By analyzing the energy dissipation caused by tangential damping, Wang et al 12 presented an active sensing method for quantitative monitoring of the bolt looseness.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…They demonstrated the superiority of MPE-based DI at higher bolt preloads by comparing it with energy-based DI for single and multi-bolt connections. Jiang et al 67 extracted nonlinear features from the received stress wave signals to overcome saturation while monitoring looseness in bolted joints. They adopted recurrence quantification analysis (RQA) for looseness monitoring and recursive plots (RP), and phase trajectory for tracing the location of loose bolts in the bolted assembly.…”

Section: Overview Of Traditional Methods For Looseness Detection Of B...mentioning

confidence: 99%

Review on recent advances in structural health monitoring paradigm for looseness detection in bolted assemblies

Chelimilla

Chinthapenta

Kali

et al. 2023

Structural Health Monitoring

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The integrity of bolted joints is still a challenging problem owing to the gross or localized slip at the interfacial surfaces of the joints when subjected to external disturbances such as vibrations. This slip escalates the interfacial movement and, thus, leads to a decrease in preload levels, that is, looseness of the bolted assemblies. In the last decade, modal analysis, wave propagation, and percussion methods were traditionally used to detect looseness in bolted connections. With an increase in computational power, machine learning algorithms such as neural networks, random forests, decision trees, and support vector machines complemented the traditional methods in accurate looseness estimation. Subsequently, this integration paved the path for real-time health monitoring of bolted joints. This paper summarizes recent investigations on looseness detection in bolted assemblies based on traditional methods and machine learning algorithms. The working principle, advantages, challenges, and applications of the aforementioned methods are also detailed in this paper. Apart from these investigations, the latest studies on the Internet of Things-based health monitoring of structures are also reviewed to explore their adaptability in remote monitoring of bolted connections for damage detection in the future.

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“…According to the different positions where PZT patches are pasted, there are three common installation methods, as shown in Figure 1. The first is to paste PZT patch onto the bolt connection mechanism, mainly using ultrasonic measurement method [1][2], vibration analysis method [3][4], piezoelectric active sensing method [5][6] and electromechanical impedance method [7][8] to monitor the bolt looseness. The second is to paste PZT onto the washer to make smart washer [9][10][11], and identify the bolt looseness by monitoring the change of axial preload of the bolt.…”

Section: Introductionmentioning

confidence: 99%

Design and Simulation of Smart Bolt Based on Zno Piezoelectric Thin Film Sensor

Cui

Yin

et al. 2022

Proceedings of the 2022 International Conference on Smart Manufacturing and Material Processing (SMMP2022)

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In order to solve the coupling error problem of smart bolt, the coupling model of piezoelectric thin film sensor and bolt is established by using piezoelectric constitutive equation. The size parameters of ZnO piezoelectric film sensor are simulated and analyzed by finite element method using Comsol software M10×1.5 piezoelectric thin film sensor is prepared on the bolt. First, the sensors of different piezoelectric materials are compared, and then the shape, size and thickness of the piezoelectric materials of the sensor are simulated; the size and thickness of electrode material are simulated. The simulation results show that the piezoelectric material is ZnO, the shape is round, the piezoelectric material size is 10 mm, the piezoelectric thickness is 1000 nm, the electrode material size is 6 mm, and the electrode thickness is 200 nm. ZnO piezoelectric thin film sensor was fabricated with this material and size.

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Multi-bolt looseness state monitoring using the recursive analytic based active sensing technique

Cited by 23 publications

References 35 publications

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

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

Review on recent advances in structural health monitoring paradigm for looseness detection in bolted assemblies

Design and Simulation of Smart Bolt Based on Zno Piezoelectric Thin Film Sensor

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