| Structural Monitoring and Maintenance Volume 1, Number 3, September 2014, pages 249-271 DOI: http://dx.doi.org/10.12989/smm.2014.1.3.249 | |
Structural monitoring of movable bridge mechanical components for maintenance decision-making | |
Mustafa Gul, Taha Dumlupinar, Hiroshi Hattori and Necati Catbas
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| Abstract [Full Text] | |
| This paper presents a unique study of Structural Health Monitoring (SHM) for the maintenance decision making about a real life movable bridge. The mechanical components of movable bridges are maintained on a scheduled basis. However, it is desired to have a condition-based maintenance by taking advantage of SHM. The main objective is to track the operation of a gearbox and a rack-pinion/open gear assembly, which are critical parts of bascule type movable bridges. Maintenance needs that may lead to major damage to these components needs to be identified and diagnosed timely since an early detection of faults may help avoid unexpected bridge closures or costly repairs. The fault prediction of the gearbox and rack-pinion/open gear is carried out using two types of Artificial Neural Networks (ANNs): 1) Multi-Layer Perceptron Neural Networks (MLP-NNs) and 2) Fuzzy Neural Networks (FNNs). Monitoring data is collected during regular opening and closing of the bridge as well as during artificially induced reversible damage conditions. Several statistical parameters are extracted from the time-domain vibration signals as characteristic features to be fed to the ANNs for constructing the MLP-NNs and FNNs independently. The required training and testing sets are obtained by processing the acceleration data for both damaged and undamaged condition of the aforementioned mechanical components. The performances of the developed ANNs are first evaluated using unseen test sets. Second, the selected networks are used for long-term condition evaluation of the rack-pinion/open gear of the movable bridge. It is shown that the vibration monitoring data with selected statistical parameters and particular network architectures give successful results to predict the undamaged and damaged condition of the bridge. It is also observed that the MLP-NNs performed better than the FNNs in the presented case. The successful results indicate that ANNs are promising tools for maintenance monitoring of movable bridge components and it is also shown that the ANN results can be employed in simple approach for day-to-day operation and maintenance of movable bridges. | |
| Key Words | |
| monitoring; maintenance; movable bridges; artificial neural networks; anomaly detection | |
| Address | |
| Mustafa Gul:Department of Civil and Environmental Engineering, University of Alberta,9105 116th St. Edmonton, Alberta, Canada Edmonton, Alberta, Canada Taha Dumlupinar and Necati Catbas:Department of Civil Environmental and Construction Engineering, University of Central Florida,12800 Pegasus Drive, Suite 211, Orlando, Florida FL, USA Hiroshi Hattori:Department of Civil and Earth Resources Engineering, Kyoto University, Saikyo-ku, Kyoto City, Japan | |
