Cloud-Based Asset Monitoring and Predictive Maintenance in an Industrial IoT System

Daji, Darian; Ghule, Kedar; Gagdani, Sarthak; Butala, Akash; Talele, Pratvina; Kamat, Hrishikesh

doi:10.1109/incet49848.2020.9154148

Cited by 4 publications

(2 citation statements)

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“…These outcomes highlight the potential of IoT in transforming maintenance practices across various sectors, leading to more sustainable operations and improved system longevity. Daji et al (2020) explore the architectural innovations brought about by IoT in predictive maintenance. Their research delves into cloud-based asset monitoring and predictive maintenance within an industrial IoT system, showcasing how IoT technologies facilitate real-time monitoring and anomaly detection in continuously operating machines.…”

Section: Introduction the Emergence Of Iot In Predictive Maintenance:...mentioning

confidence: 99%

“…Their research delves into cloud-based asset monitoring and predictive maintenance within an industrial IoT system, showcasing how IoT technologies facilitate real-time monitoring and anomaly detection in continuously operating machines. This approach not only reduces maintenance downtime but also enhances the efficiency of industrial processes (Daji et al, 2020). The integration of IoT with modern concepts like machine learning, data visualization, and cloud computing marks a significant milestone in the evolution of predictive maintenance.…”

Section: Introduction the Emergence Of Iot In Predictive Maintenance:...mentioning

confidence: 99%

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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

et al. 2024

Comput. sci. IT res. j.

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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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Section: Introduction the Emergence Of Iot In Predictive Maintenance:...mentioning

confidence: 99%

Section: Introduction the Emergence Of Iot In Predictive Maintenance:...mentioning

confidence: 99%