Development of Ultrasonic Axial Bolting Force Inspection System for Turbine Bolts in Thermal Power Plants

Suda, Masataka; Hasuo, Yasunori; Kanaya, Akihiro; Ogura, Yukio; Takishita, Toshio; Suzuki, Yoshiaki

doi:10.1299/jsmea1988.35.2_216

Cited by 15 publications

(9 citation statements)

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“…At the early age when the bolt starts to lose preload, the rotation angle between the bolt and nut is very small, and thus that it is very difficult to find a rapid and userfriendly approach that could determine the health condition of the bolted connection in the field. Currently, a few methods have been reported in the literature for bolt monitoring, including acoustoelastic effect-based method [15][16][17][18][19], piezoelectric impedance-based method [20][21][22][23][24][25][26], piezoelectric active sensing method [12,[27][28][29][30], fiber optic sensor based method [31][32][33], and vibration-based method [34][35][36]. Though these methods have shown great potentials to monitoring the bolt looseness condition in or near real time, there is still a drawback of requiring installation or at least close, sustained contact of the transducer with the inspected structure [37,38].…”

Section: Introductionmentioning

confidence: 99%

Tapping and listening: a new approach to bolt looseness monitoring

Kong

Zhu

2018

Smart Mater. Struct.

117

101

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Bolted joints are among the most common building blocks used across different types of structures, and are often the key components that sew all other structural parts together. Monitoring and assessment of looseness in bolted structures is one of the most attractive topics in mechanical, aerospace, and civil engineering. This paper presents a new percussion-based non-destructive approach to determine the health condition of bolted joints with the help of machine learning. The proposed method is very similar to the percussive diagnostic techniques used in clinical examinations to diagnose the health of patients. Due to the different interfacial properties among the bolts, nuts and the host structure, bolted joints can generate unique sounds when it is excited by impacts, such as from tapping. Power spectrum density, as a signal feature, was used to recognize and classify recorded tapping data. A machine learning model using the decision tree method was employed to identify the bolt looseness level. Experiments demonstrated that the newly proposed method for bolt looseness detection is very easy to implement by ‘listening to tapping’ and the monitoring accuracy is very high. With the rapid in robotics, the proposed approach has great potential to be implemented with intimately weaving robotics and machine learning to produce a cyber-physical system that can automatically inspect and determine the health of a structure.

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Section: Introductionmentioning

confidence: 99%

Tapping and listening: a new approach to bolt looseness monitoring

Kong

Zhu

2018

Smart Mater. Struct.

117

101

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“…The acoustoelastic coefficients AL and AT are negative, which indicates that the wave velocity decreases, while the stress increases. Measurements made to determine the acoustoelastic constant for certain kinds of steel bolts revealed that the acoustoelastic constant is independent of any effects that heat treatment may have on the bolts [14].…”

Section: Acoustoelastic Effect Based Methodsmentioning

confidence: 99%

Review of Bolted Connection Monitoring

Wang

Song

Liu

et al. 2013

International Journal of Distributed Sensor Networks

125

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This paper reviews the research of monitoring technologies for bolted structural connections. The acoustoelastic effect based method, the piezoelectric active sensing method, and the piezoelectric impedance method are the three commonly used to monitor bolted connections. The basic principle and the applications of these three methods are discussed in detail in this paper. In addition, this paper presents a comparison of these methods and discusses their suitability for in situ or real-time bolt connection monitoring.

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“…At present, the mainstream bolt loosening detection methods are mainly manual, inspection acoustic, guided wave and piezoelectric impedance methods (SUDA et al, 1992; Huynh et al, 2017). For example, Yang et al (2006) proposed a diagnostic method for bolt loosening in C-C composite thermal protection plates based on the acoustic attenuation of ultrasonic waves at the bolt connection.…”

Section: Introductionmentioning

confidence: 99%

Image-based bolt self-localization and bolt-loosening detection using deep learning and an improved homography-based prospective rectification method

Xie

Luo

Tang

et al. 2023

Advances in Structural Engineering

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The bolt loosening detection method has been paid attention by engineering and academic scholars. The presented methods only focus on the identification of the bolt based on deep learning, and the problem of bolt localization and the influence of shadow on distortion correction is seldom studied. In this paper, a bolt self-localization method based on YOLOv4 deep learning algorithm is proposed. A bolt numbering rule is established and the deep learning is introduced to identify the number and locate the bolt. A new square bolt gasket is proposed and four corner points are used for distortion correction. In order to reduce the influence of shadows, a grayscale enhancement strategy is proposed to improve the correction stability. Finally, a laboratory flange joint is used to verify the proposed method. The results show that the bolt self-localization method is feasible and the new bolt gasket can effectively improve the stability of distortion correction and bolt loosening detection.

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Development of Ultrasonic Axial Bolting Force Inspection System for Turbine Bolts in Thermal Power Plants

Cited by 15 publications

References 0 publications

Tapping and listening: a new approach to bolt looseness monitoring

Tapping and listening: a new approach to bolt looseness monitoring

Review of Bolted Connection Monitoring

Image-based bolt self-localization and bolt-loosening detection using deep learning and an improved homography-based prospective rectification method

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