Case Studies
Oct 13, 2023

Monitoring of Gantry Crane Track Health with Commodity IOT Devices

Publication: Journal of Infrastructure Systems
Volume 29, Issue 4

Abstract

The global supply chain depends on the efficient transfer of containers between cargo ships and ground transport. To ensure reliable operations, equipment at ports requires predictive maintenance, with minimal downtime, at scheduled times. One common piece of equipment, rail mounted automated gantry (RMG) automatic stacking cranes (ASC), can be outfitted with commodity acceleration sensors to better monitor the structural health of the crane and the rails on which the crane runs. The ratio of the rolling standard deviation of the crane vibration at speed versus at rest is a metric that can separate the cranes and rails into those that are in need of detailed inspection and repair, and those that are in good health. This method produced zero false negative, and an even split of true positives and false positives. The cost of a false positive is orders of magnitude less than the cost of a false negative, so this technique should help bend the cost curve down on RMG maintenance.

Practical Applications

Industrial RMG ASCs and the rails on which they run are difficult and expensive to survey, but abnormalities can cause damage to the cranes that will be much more expensive and difficult to fix. We are developing a method using low cost computing in the form of Raspberry Pis and commodity acceleration sensors to detect abnormalities without disturbing the use of the crane for a survey. One limitation of low cost sensors is that they produce a signal with a relatively high amount of noise. However, the signal is usable. We quantify the noise level as the rolling standard deviation (RSD) of the raw signal from the smoothed signal, which can be calculated for the crane at rest and at different speeds. The ratio of resting RSD to moving RSD is used to classify a rail as needing attention or as nominal. That ratio can be compared between two cranes that are operating on the same set of rails to determine whether the abnormalities are caused by the crane or by the rail. Utilizing this metric to prioritize track repairs such as tamping or grinding, or crane repairs such as wheel replacement or bogie alignments can significantly reduce the maintenance cost for the overall crane systems.

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Data Availability Statement

Some or all data, models, or code used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments.

Acknowledgments

The authors would like to acknowledge the Port of Virginia for making the cranes available for adding sensors, sharing maintenance and cost data with the research team, providing computers and sensors, and making maintenance, engineering, and rail repair teams available for interviews. Requests for data should be made to the port via their website: https://www.portofvirginia.com/ and all raw data may be provided at the sole discretion of Virginia International Terminals LLC. The code used for this paper is on GitHub and is included at (Hendrickson 2022). The authors acknowledge William & Mary Research Computing for providing computational resources and/or technical support that have contributed to the results reported within this paper. URL: https://www.wm.edu/it/rc.

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Information & Authors

Information

Published In

Go to Journal of Infrastructure Systems
Journal of Infrastructure Systems
Volume 29Issue 4December 2023

History

Received: Jan 4, 2023
Accepted: Aug 24, 2023
Published online: Oct 13, 2023
Published in print: Dec 1, 2023
Discussion open until: Mar 13, 2024

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Authors

Affiliations

Ph.D. Candidate, Dept. of Applied Science, William & Mary, Williamsburg, VA 23187; Vice President, Asset Management, Virginia International Terminals, LLC, 601 World Trade Center, Norfolk, VA 23510 (corresponding author). ORCID: https://orcid.org/0000-0003-0672-5064. Email: [email protected]; [email protected]; [email protected]
Mark K. Hinders
Professor, Dept. of Applied Science, William & Mary, Williamsburg, VA 23187.

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