Abstract
Functionality of movable bridge highly depends on the performance of the mechanical components including gearbox and motor. Therefore, on-going maintenance of these components are extremely important for uninterrupted operation of movable bridges. Unfortunately, there have been only a few studies on monitoring of mechanical components of movable bridges. As a result, in this study, a statistical framework is proposed for continuous maintenance monitoring of the mechanical components. The efficiency of this framework is verified using long-term data that has been collected from both gearbox and motor of a movable bridge. In the first step, critical features are extracted from massive amount of Structural Health Monitoring (SHM) data. Next, these critical features are analyzed using Moving Principal Component Analysis (MPCA) and a condition-sensitive index is calculated. In order to study the efficiency of this framework, critical maintenance issues have been extracted from the maintenance reports prepared by the maintenance personnel and compared against the calculated condition index. It has been shown that there is a strong correlation between the critical maintenance actions, reported individually by maintenance personnel, and the condition index calculated by proposed framework and SHM data. The framework is tested for the gearbox.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Phares, B.M., Washer, G.A., Rolander, D.D., Graybeal, B.A., Moore, M.: Routine highway bridge inspection condition documentation accuracy and reliability. J. Bridge Eng. 9(4), 403–413 (2004)
Catbas, F.N., Malekzadeh, M., Khuc, T.: Movable Bridge Maintenance Monitoring. Florida Department of Transportation, Tallahassee (2013)
Malekzadeh, M., Atia, G., Catbas, F.N.: Performance-based structural health monitoring through an innovative hybrid data interpretation framework. J. Civ. Struct. Heal. Monit. 5(3), 287–305 (2015)
Catbas, N., Malekzadeh, M., Gul, M., Kwon, I.B.: An integrated approach for structural health monitoring using an in-house built fiber optic system and non-parametric data analysis. Smart Struct. Syst. 14(5), 917 (2014)
Malekzadeh, M., Catbas, F.N.: A comparative evaluation of two statistical analysis methods for damage detection using fibre optic sensor data. Int. J. Reliab. Saf. 8(2–4), 135–155 (2014)
Laory, I., Trinh, T.N., Smith, I.F.: Evaluating two model-free data interpretation methods for measurements that are influenced by temperature. Adv. Eng. Inf. 25(3), 495–506 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 The Society for Experimental Mechanics, Inc.
About this paper
Cite this paper
Malekzadeh, M., Catbas, F.N. (2016). A Machine Learning Framework for Automated Functionality Monitoring of Movable Bridges. In: Pakzad, S., Juan, C. (eds) Dynamics of Civil Structures, Volume 2. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-29751-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-29751-4_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-29750-7
Online ISBN: 978-3-319-29751-4
eBook Packages: EngineeringEngineering (R0)