Image-Based Monitoring Of Open Gears Of Movable Bridges For Condition Assessment And Maintenance Decision Making

Keywords

Computer vision; Condition; Edge detection; Image processing; Maintenance; Movable bridge; Neural network; Open gear; Structural health monitoring; Video camera

Abstract

Movable bridges are unique structures due to the complex interaction between their structural, mechanical, and electrical systems with an intricate interrelation creating several challenges related to operation and maintenance. Continuous monitoring of the critical parts of these structures is essential to track and evaluate their performance for improving maintenance operations and reducing the associated costs. Open gears are one of the most critical components of movable bridges. Proper and regular maintenance of these gears is vitally important to ensure a safe, reliable, and cost-effective operation. In this study, a practical and low-cost monitoring approach is presented to track the lubrication level in an open gear of a movable bridge by using video cameras. Two unique indices are developed for monitoring of the open gear by investigating two different image processing methods in a comparative fashion. The first methodology is based on an edge detection algorithm that utilizes a Sobel gradient operator to determine the edges in the open gear image. A lubrication index (LI) based on the edge detection results is defined and extracted to determine the lubrication level. The second methodology employs a fuzzy neural network-based approach to define a lubrication anomaly parameter (LAP) for assessing the lubrication level. The analysis results from the real-life application show that both methodologies successfully identify the lubrication level of the movable bridge's open gear.

Publication Date

3-1-2015

Publication Title

Journal of Computing in Civil Engineering

Volume

29

Issue

2

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1061/(ASCE)CP.1943-5487.0000307

Socpus ID

84923094207 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84923094207

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