AI-Driven Predictive Maintenance in IoT-Enabled Industrial Systems

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@article{1701235,
      author = {Thejaswi Adimulam, Manoj Bhoyar, Purushotham Reddy},
      title = {AI-Driven Predictive Maintenance in IoT-Enabled Industrial Systems},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {2},
      number = {11},
      pages = {398-410},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1701235.pdf},
      abstract = {In the era of Industry 4.0, the integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies has revolutionized industrial maintenance practices. This paper presents a comprehensive review and analysis of AI-driven predictive maintenance in IoT-enabled industrial systems. We explore the synergies between AI algorithms and IoT sensor networks in predicting equipment failures, optimizing maintenance schedules, and enhancing overall system reliability. The study covers various AI techniques, including machine learning, deep learning, and reinforcement learning, applied to predictive maintenance. We also discuss the challenges and opportunities in implementing these technologies across different industrial sectors. Our findings indicate that AI-driven predictive maintenance significantly reduces downtime, cuts maintenance costs, and improves the longevity of industrial equipment. The paper concludes with future research directions and potential implications for industry practitioners.},
      keywords = {Artificial Intelligence; Internet of Things; Predictive Maintenance; Industry 4.0; Machine Learning; Industrial Systems},
      month = {May},
  }