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In high-security environments, the integration of wireless sensor networks (WSN) and deep learning algorithms revolutionizes predictive maintenance, enabling real-time data collection and proactive identification of equipment failures. This synergy enhances operational efficiency and fortifies security protocols by addressing maintenance needs pre-emptively. The seamless fusion of WSN and Deep Learning marks a paradigm shift in predictive maintenance, reinforcing the resilience of critical infrastructure in high-security domains. This paper mainly explores the predictive maintenance in a high-security area using WSN and deep learning algorithms and the optimization techniques which can used for the whole system.

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Optimizing Predictive Maintenance in a High-Security Area Using WSN and Deep Learning Algorithms

  • 会议论文

  • 发表日期：

    2025年

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  • Peddi Niranjan Reddy,
  • Mruthyunjayam Allakonda,
  • Gujjula Anjareddy,
  • Bimmarolu Shanthi,
  • Renuka Kuntala,
  • K. Venkata Balamurali Krishna

摘要

In high-security environments, the integration of wireless sensor networks (WSN) and deep learning algorithms revolutionizes predictive maintenance, enabling real-time data collection and proactive identification of equipment failures. This synergy enhances operational efficiency and fortifies security protocols by addressing maintenance needs pre-emptively. The seamless fusion of WSN and Deep Learning marks a paradigm shift in predictive maintenance, reinforcing the resilience of critical infrastructure in high-security domains. This paper mainly explores the predictive maintenance in a high-security area using WSN and deep learning algorithms and the optimization techniques which can used for the whole system.