Title: IoT and machine learning-enabled intelligent full-process reliability management for equipment

Authors: Yueheng Sun; Yingpei Xia; Wenhui Dong; Xiangyu Wang; Jigang Zhang

Addresses: Shandong Nuclear Power Company, Yantai, 265116, China ' Shandong Nuclear Power Company, Yantai, 265116, China ' Shandong Nuclear Power Company, Yantai, 265116, China ' Shandong Nuclear Power Company, Yantai, 265116, China ' Shandong Nuclear Power Company, Yantai, 265116, China

Abstract: As the internet of things (IoT) and machine learning (ML) technologies grow quickly, managing equipment is becoming smarter. When it comes to handling diverse data from various sources and making informed maintenance decisions promptly, traditional methods of operating and maintaining equipment have clear limitations. This paper suggests a full-process equipment reliability intelligent management model (HMIF) based on IoT and ML. It combines data from various sources and develops a multi-stage intelligent algorithm system to create a closed-loop management system, spanning from monitoring equipment status to making informed maintenance decisions. The performance evaluation and training tests on two datasets show that HMIF is both effective and dependable. The research results demonstrate a highly accurate and adaptable approach to intelligent maintenance of industrial equipment, which is particularly useful in engineering applications.

Keywords: internet of things; IoT; ML; intelligent management of reliability; multimodal data fusion.

DOI: 10.1504/IJSN.2025.150133

International Journal of Security and Networks, 2025 Vol.20 No.4, pp.255 - 266

Received: 28 Jun 2025
Accepted: 17 Jul 2025
Published online: 01 Dec 2025 *

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