ENHANCING MAINTENANCE AND RELIABILITY OF FITNESS EQUIPMENT USING REINFORCED ANT COLONY ALGORITHM FOR FAULT PREDICTION

Authors

  • Jing Qi Yang Department of Mechanical and Electronic Engineering, Qingdao University of Technology, Qingdao, 266520, China,
  • Yuan Hao Zou Department of Mechanical and Electronic Engineering, Qingdao University of Technology, Qingdao, 266520, China
  • Zhao Zheng Jiang Department of Mechanical and Electronic Engineering, Qingdao University of Technology, Qingdao, 266520, China,

Keywords:

Fault Prediction Model, Automated Machinery, Equipment, Reinforced, Ant Colony Algorithm

Abstract

This research explores the application of the reinforced ant colony algorithm to enhance fault prediction and maintenance strategies for fitness equipment. By adapting this swarm intelligence algorithm, traditionally used in manufacturing and logistics, to the context of sports fitness, the study offers a novel approach to scheduling and predictive maintenance tasks. This method not only addresses complex optimization problems effectively but also improves the adaptability and efficiency of maintenance operations in sports facilities. The algorithm's enhanced capabilities allow for better management of equipment maintenance schedules, ensuring higher availability and reliability of fitness apparatuses, ultimately supporting athlete training regimes by minimizing equipment downtime.

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Published

2023-10-02

Issue

Vol. 23 No. 93 (2023): Vol. 23, No. 93, November/ December 2023

Section

Articles