Academic Journal

A Distributed Vision-Based System for Cost-Effective Fault Diagnosis of Modular Bridge Expansion Joints.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: A Distributed Vision-Based System for Cost-Effective Fault Diagnosis of Modular Bridge Expansion Joints.
Συγγραφείς: Huang, Haoxiang, Wang, Dalei, Pan, Yue, Ma, Minglei, Chen, Airong, Li, Jun
Πηγή: Structural Control & Health Monitoring; 4/30/2026, Vol. 2026, p1-17, 17p
Θεματικοί όροι: Fault diagnosis, Structural health monitoring, Structural dynamics, Image enhancement (Imaging systems), Robot vision, Distributed sensors
Περίληψη: Detecting faults in modular bridge expansion joints (MBEJs) is a critical challenge, as their complex dynamic behavior under operational loads is difficult to characterize with conventional methods. Existing monitoring approaches often face a trade-off between high cost, intrusive installation, and an inability to capture the distributed kinematic patterns essential for fault diagnosis. To address this gap, this paper proposes a cost-effective, distributed vision-based framework designed for fault diagnosis of in-service MBEJs. The framework employs a single camera to synchronously track an array of fiducial markers, enabling noncontact, multipoint dynamic measurement, while its algorithmic pipeline synergizes image enhancement with robust detection to ensure data reliability. Field deployment on a long-span bridge provided initial validation of its diagnostic capability by correctly classifying a known bearing failure as a structural-level anomaly, based on its distinctive dynamic signatures: a statistically significant elevation in displacement variance and the emergence of an anomalous, structure-borne vibrational mode at 3.577 Hz. The framework demonstrates its potential for unmanned anomaly detection, providing critical kinematic indicators that can serve as a basis for early warnings and proactive structural management. [ABSTRACT FROM AUTHOR]
Copyright of Structural Control & Health Monitoring is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index
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  Data: A Distributed Vision-Based System for Cost-Effective Fault Diagnosis of Modular Bridge Expansion Joints.
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  Data: <searchLink fieldCode="AR" term="%22Huang%2C+Haoxiang%22">Huang, Haoxiang</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Dalei%22">Wang, Dalei</searchLink><br /><searchLink fieldCode="AR" term="%22Pan%2C+Yue%22">Pan, Yue</searchLink><br /><searchLink fieldCode="AR" term="%22Ma%2C+Minglei%22">Ma, Minglei</searchLink><br /><searchLink fieldCode="AR" term="%22Chen%2C+Airong%22">Chen, Airong</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Jun%22">Li, Jun</searchLink>
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  Data: Structural Control & Health Monitoring; 4/30/2026, Vol. 2026, p1-17, 17p
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  Data: <searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+health+monitoring%22">Structural health monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+dynamics%22">Structural dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Image+enhancement+%28Imaging+systems%29%22">Image enhancement (Imaging systems)</searchLink><br /><searchLink fieldCode="DE" term="%22Robot+vision%22">Robot vision</searchLink><br /><searchLink fieldCode="DE" term="%22Distributed+sensors%22">Distributed sensors</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Detecting faults in modular bridge expansion joints (MBEJs) is a critical challenge, as their complex dynamic behavior under operational loads is difficult to characterize with conventional methods. Existing monitoring approaches often face a trade-off between high cost, intrusive installation, and an inability to capture the distributed kinematic patterns essential for fault diagnosis. To address this gap, this paper proposes a cost-effective, distributed vision-based framework designed for fault diagnosis of in-service MBEJs. The framework employs a single camera to synchronously track an array of fiducial markers, enabling noncontact, multipoint dynamic measurement, while its algorithmic pipeline synergizes image enhancement with robust detection to ensure data reliability. Field deployment on a long-span bridge provided initial validation of its diagnostic capability by correctly classifying a known bearing failure as a structural-level anomaly, based on its distinctive dynamic signatures: a statistically significant elevation in displacement variance and the emergence of an anomalous, structure-borne vibrational mode at 3.577 Hz. The framework demonstrates its potential for unmanned anomaly detection, providing critical kinematic indicators that can serve as a basis for early warnings and proactive structural management. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Structural Control & Health Monitoring is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1155/stc/8765999
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        Text: English
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              Text: 4/30/2026
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