With the rapid advancement of intelligent manufacturing, ensuring equipment safety has become a crucial prerequisite for enterprise production. In the engineer-to-order (ETO) production mode, characterized by diverse equipment types and frequent adjustments in production lines, equipment maintenance has become increasingly complex. Traditional maintenance plans are no longer adequate to meet the evolving demands of equipment maintenance. This paper proposes a security-enhanced predictive maintenance scheme specifically designed for ETO-type production equipment. The scheme utilizes industrial Internet of Things (IIoT) technology to monitor machines and equipment, constructs prediction models using machine learning methods, and reinforces the security of the prediction system through adoption of a decentralized architecture with blockchain distributed storage. In this experiment, six supervised learning models were compared, and it was found that the model based on the random forest algorithm achieved an outstanding accuracy rate of 98.88%. Furthermore, the average total response time for generating predictions within the system is 2.0 s, demonstrating a performance suitable for practical equipment maintenance applications.

中文翻译：

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适用于 ETO 制造的安全增强型设备预测维护解决方案

随着智能制造的快速推进，保障设备安全已成为企业生产的重要前提。在按订单设计（ETO）生产模式下，设备类型多样，生产线调整频繁，设备维护变得越来越复杂。传统的维护计划已不足以满足不断变化的设备维护需求。本文提出了一种专门针对ETO型生产设备设计的安全增强预测维护方案。该方案利用工业物联网（IIoT）技术来监控机器和设备，利用机器学习方法构建预测模型，并通过采用区块链分布式存储的去中心化架构来增强预测系统的安全性。在本实验中，对六种监督学习模型进行了比较，发现基于随机森林算法的模型取得了高达 98.88% 的出色准确率。此外，系统内生成预测的平均总响应时间为 2.0 秒，证明了适合实际设备维护应用的性能。