REVIEW ON THE EVOLUTION AND IMPACT OF IOT-DRIVEN PREDICTIVE MAINTENANCE: ASSESSING ADVANCEMENTS, THEIR ROLE IN ENHANCING SYSTEM LONGEVITY, AND SUSTAINABLE OPERATIONS IN BOTH MECHANICAL AND ELECTRICAL REALMS

Joachim Osheyor Gidiagba, Nwabueze Kelvin Nwaobia, Preye Winston Biu, Chinedu Alex Ezeigweneme, Aniekan Akpan Umoh
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Abstract

This study provides a comprehensive review of the evolution and impact of Internet of Things (IoT)-driven predictive maintenance, focusing on advancements in technology, their role in enhancing system longevity, and promoting sustainable operations in mechanical and electrical systems. The primary objective was to assess how IoT integration has transformed traditional maintenance approaches, leading to improved system durability and reliability. Utilizing a systematic literature review methodology, the study involved sourcing data from peer-reviewed journals, conference proceedings, and industry reports. A content analysis approach was employed to analyze the data, focusing on themes such as technological advancements, sustainability considerations, and industry-specific applications of IoT in predictive maintenance. Key findings reveal significant advancements in IoT applications, particularly the integration of advanced data analytics, artificial intelligence, and machine learning in predictive maintenance strategies. These advancements have led to more accurate and timely maintenance interventions, contributing to enhanced system longevity and operational efficiency. The study also highlights the emergence of green IoT practices and the challenges and opportunities in the future landscape of IoT in predictive maintenance. The study concludes that IoT-driven predictive maintenance is pivotal for sustainable industrial operations, with opportunities lying in addressing challenges through innovative solutions and robust regulatory frameworks. Recommendations for industry and policy include fostering sustainable IoT practices and prioritizing energy efficiency. Future research directions involve exploring the integration of IoT with emerging technologies and investigating the long-term environmental impacts of IoT deployments. Keywords: Predictive Maintenance, System Longevity, Sustainable Operations, Internet of Things.
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回顾物联网驱动的预测性维护的发展和影响:评估其进展、在提高系统寿命方面的作用,以及在机械和电气领域的可持续运行情况
本研究全面回顾了物联网(IoT)驱动的预测性维护的演变和影响,重点关注技术的进步及其在提高系统寿命和促进机电系统可持续运行方面的作用。主要目的是评估物联网集成如何改变传统的维护方法,从而提高系统的耐用性和可靠性。本研究采用系统的文献综述方法,从同行评审期刊、会议论文集和行业报告中获取数据。采用内容分析法对数据进行分析,重点关注技术进步、可持续性考虑因素以及物联网在预测性维护中的特定行业应用等主题。主要研究结果表明,物联网应用取得了重大进展,特别是在预测性维护策略中集成了先进的数据分析、人工智能和机器学习。这些进步带来了更准确、更及时的维护干预,有助于提高系统寿命和运行效率。研究还强调了绿色物联网实践的出现,以及物联网在预测性维护中的未来前景所面临的挑战和机遇。研究得出结论,物联网驱动的预测性维护对可持续工业运营至关重要,通过创新的解决方案和健全的监管框架应对挑战是机遇所在。对行业和政策的建议包括促进可持续的物联网实践和优先考虑能源效率。未来的研究方向包括探索物联网与新兴技术的整合,以及调查物联网部署对环境的长期影响。关键词预测性维护、系统寿命、可持续运营、物联网。
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