Vision-based load sway monitoring to improve crane safety in blind lifts

Yihai Fang, Jingdao Chen, Yong K. Cho, Kinam Kim, Sijie Zhang, Esau Perez

Research output: Contribution to journalArticleResearchpeer-review

4 Citations (Scopus)

Abstract

Blind lifts on congested offshore platform (OP) environments are inherently dangerous because of the substantial presence of spatial conflicts in the crane workspace and the operator’s limited visibility to the load. This research aims to improve operators’ spatial awareness in blind lifts in the OP environment through real-time crane states sensing and visualization. A technical framework is proposed that consists of two sensing modules (i.e., crane motion monitoring and load sway monitoring) and a visualization module. To augment the robustness of the inertial measurement unit (IMU)-based method, a computer vision (CV)-based approach was introduced to track load positions for load sway monitoring. A prototype system was built and tested in an offshore platform to validate the crane motion monitoring and visualization modules. A series of lab and field experiments were conducted to evaluate the accuracy and robustness of CV-based load sway monitoring in control and real-world scenarios. The results from the field and lab tests indicate the proposed framework and methods were able to accurately monitor and visualize the crane states in real-time and thus provide the operator with adequate assistance to identify and mitigate unsafe conditions during blind lifts.

Original languageEnglish
Pages (from-to)233-242
Number of pages10
JournalJournal of Structural Integrity and Maintenance
Volume3
Issue number4
DOIs
Publication statusPublished - 2 Oct 2018

Keywords

  • blind lift
  • computer vision
  • Crane operation
  • sensing
  • visualization

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