{"id":8917,"date":"2026-07-03T17:41:36","date_gmt":"2026-07-03T09:41:36","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=8917"},"modified":"2026-07-03T17:57:53","modified_gmt":"2026-07-03T09:57:53","slug":"jase-202610-33-027","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202610-33-027","title":{"rendered":"Diagnostic Framework for Smart Manufacturing Systems Driven by Intelligent Embedded IoT"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 33<\/a><\/p>\n<\/div>\n\n\n\n

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Ranran Zhao<\/i><\/a><\/p>\n\n\n\n

School of Intelligent Manufacturing, Jiangsu Vocational College of Electronics and Information, Huaian, Jiangsu,223003, China<\/p>\n<\/div>\n\n\n\n

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Received: April 6, 2026
Accepted:\u00a0May 18, 2026
Publication Date:\u00a0July 3, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Components of IoT in Smart\u00a0Manufacturing.\u00a0<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202610_33.027<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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The proposed Hybrid IoT framework combines edge and cloud intelligence to enable predictive maintenance and optimization in smart manufacturing systems. Edge computing supports real-time processing for fast anomaly detection, while cloud computing provides high computational power for deep analysis, model training, and long-term decision-making. The framework is adaptable to diverse industrial environments with varying hardware and communication technologies, ensuring scalability and flexibility. It integrates advanced AI methods to enhance interpretability and ensure transparent, reliable system decisions. Privacy-preserving techniques protect sensitive industrial data during transmission and processing. Overall, this hybrid approach offers a secure, efficient, scalable, and robust solution that improves performance, reliability, and sustainability in modern smart manufacturing applications.<\/p>\n\n\n\n

Keywords:\u00a0Hybrid IoT Framework, Predictive Maintenance, Edge Computing, Cloud Computing, Real-Time Fault Detection<\/em><\/p>\n\n\n\n

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