Identification of loosen bolt of transmission tower based on wavelet packet analysis and neural net

Tan, Dongmei; Qu, Weilian; Tu, Jianwei; Qin, Wenke

doi:10.1109/icetce.2011.5775756

Cited by 4 publications

References 11 publications

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A deep‐learning approach for health monitoring of a steel frame structure with bolted connections

Pal

Sikdar

Banerjee

2021

Structural Contr & Hlth

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This study is motivated by the need to develop a data-driven deep-learning approach for vibration-based structural health monitoring of a steel frame structure with bolted connections. A convolutional-neural-network-based deep-learning architecture is designed and trained to extract discriminative features from the vibration-based time-frequency scalogram images and use those to distinguish the undamaged and damaged cases of the targeted frame structure. Different damage and undamaged classes corresponding to the loosening of bolts are categorized as fully loose, hand tight, and fully tight (undamaged) conditions. The average training and validation accuracy were found to be 100% and 98.1%, respectively. In order to check the performance and robustness of the technique, testing is carried out for an unseen dataset corresponding to the training classes as well as some additional cases close to the training classes. The proposed deep-learning approach can successfully classify the damage classes with high testing accuracy that demonstrates its efficacy as an automation tool for health monitoring of connections of plane frame structures.convolutional neural network, deep learning, loosening of bolts, steel frame structure, structural health monitoring | INTRODUCTIONDue to the higher material reliability property and speed of construction, structural steel is extensively used to construct bridges, buildings, industrial structures, and offshore structures. The members are generally connected by welds or bolts. These bolted joints act as semi-rigid connections, which absorb significant energy under the excitation of wind and/or earthquake forces. However, because of energy absorption, the bolts may get loosened, which changes the subsequent behavior of the structure.A review on vibration-based damage detection methods in civil engineering structures with the applications from traditional to machine learning (ML) to deep-learning techniques is presented in Avci et al. 1 Various traditional techniques for structural health monitoring (SHM) of different type of civil engineering structures are discussed in previous studies. [2][3][4][5][6][7] Some applications of traditional SHM techniques on frame structures are studied and reported in other studies. 3,5,8,9 Modern signal processing tools like wavelet transform are widely utilized on various responses of frame structures for identification and quantification of damage. [10][11][12][13] The model updating based damage detection techniques are found to be gained popularity in the area of SHM for the last two decades. Some studies on various framed structures

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A deep‐learning approach for health monitoring of a steel frame structure with bolted connections

Pal

Sikdar

Banerjee

2021

Structural Contr & Hlth

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Diagnosis of Bolt Loosening Fault in Structures with a Novel Second-Order Output Spectrum based Method

Jing

2018

2018 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

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Performance of damage detection algorithms for health monitoring of joints in steel frame structures using vibration-based techniques

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Identification of loosen bolt of transmission tower based on wavelet packet analysis and neural net

Cited by 4 publications

References 11 publications

A deep‐learning approach for health monitoring of a steel frame structure with bolted connections

A deep‐learning approach for health monitoring of a steel frame structure with bolted connections

Diagnosis of Bolt Loosening Fault in Structures with a Novel Second-Order Output Spectrum based Method

Performance of damage detection algorithms for health monitoring of joints in steel frame structures using vibration-based techniques

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