Vol. 5 No. 1 (2025): Issue 5
Articles

An Efficient Bayesian Networks-based Approach for Aircraft Sensor Placement Optimization

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  • Jing Li,
  • Osman Kaya,
  • Wei Chen,
  • Meng Zhao
Jing Li
School of Information and Control Engineering, Xiangtan University, Xiangtan, 411105, China
Wei Chen
Institute of Aeronautics and Astronautics, Hunan Institute of Science and Technology, Yueyang, 414000, China
Meng Zhao
Center for Computational Intelligence, Changzhou Institute of Technology, Changzhou, 213002, China

Published 2025-06-08

Keywords

  • Aircraft,
  • Sensor Placement,
  • Bayesian Networks,
  • System Reliability,
  • Optimization Techniques

How to Cite

Li, J., Kaya, O., Chen, W., & Zhao, M. (2025). An Efficient Bayesian Networks-based Approach for Aircraft Sensor Placement Optimization. Journal of Robotic Systems and Design, 5(1). Retrieved from https://ojs.mri-pub.com/index.php/JRSD/article/view/123

Abstract

This research paper presents an innovative approach for optimizing aircraft sensor placement through Bayesian networks. The importance of sensor placement optimization in ensuring aircraft system reliability and safety is crucial. Current research in this field faces challenges such as computational complexity and limited accuracy in predicting optimal sensor locations. To address these issues, this paper introduces a novel method that leverages Bayesian networks to efficiently optimize the placement of sensors on aircraft components. By integrating probabilistic graphical models and machine learning techniques, this approach offers a promising solution for enhancing sensor placement strategies in the aviation industry. The proposed methodology aims to improve the reliability and performance of aircraft systems while reducing maintenance costs and enhancing overall safety measures.

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