Industrial IoT-driven condition-based maintenance plus for complex system with multiple dependencies

IF 9.9 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics Pub Date : 2025-10-08 DOI:10.1016/j.aei.2025.103939

Yaohui Lu , Shaoping Wang , Chao Zhang , Rentong Chen , Hongyan Dui , Yuning Wang

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引用次数: 0

Abstract

Maintenance management based on industrial Internet of Things (IoT) can significantly increase the efficiency of complex system maintenance and enable a transformation from reactive response to proactive prevention. However, conventional condition-based maintenance (CBM) tends to be single-component independent maintenance, which cannot perform collaborative optimization of multi-component maintenance and resource scheduling. In addition, due to shared resources and sequential workflows, the dependencies of the maintenance processes between components makes the existing CBM models not applicable. To solve the aforementioned problem, this paper proposes an industrial IoT-driven condition-based maintenance plus (CBM+) method allowing to perform collaborative optimization of proactive maintenance activities for complex systems. Firstly, the real-time remaining useful life of components is predicted based on degradation data monitored by IoT sensors. Secondly, considering the economic-functional-maintenance process dependencies, a multi-component opportunistic maintenance strategy, based on hierarchical Bayesian networks and multi-layer maintenance process networks, is proposed to increase the collaborative maintenance capacity. Afterwards, leveraging an industrial IoT-enhanced field management approach, the maintenance elements (human, equipment, material, method, and environment) are systematically managed to optimize the maintenance efficiency. Furthermore, an industrial IoT-driven CBM+ optimization model considering multiple dependencies and maintenance elements is developed. Finally, a case study of an industrial IoT-driven hydraulic system is conducted to demonstrate the proposed maintenance strategy.

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工业物联网驱动的基于状态的维护加上具有多个依赖关系的复杂系统

基于工业物联网的维护管理可以显著提高复杂系统的维护效率，实现从被动响应到主动预防的转变。然而，传统的基于状态的维修往往是单部件的独立维修，无法实现多部件维修和资源调度的协同优化。此外，由于共享资源和顺序工作流，组件之间维护过程的依赖性使得现有的CBM模型不适用。为了解决上述问题，本文提出了一种工业物联网驱动的基于状态的维护+ (CBM+)方法，允许对复杂系统的主动维护活动进行协同优化。首先，基于物联网传感器监测的退化数据，实时预测部件的剩余使用寿命。其次，考虑到经济-功能-维修过程的依赖性，提出了一种基于分层贝叶斯网络和多层维修过程网络的多组件机会维修策略，以提高协同维修能力；然后，利用工业物联网增强的现场管理方法，对维护要素（人、设备、材料、方法和环境）进行系统管理，以优化维护效率。在此基础上，建立了考虑多种依赖关系和维护要素的工业物联网驱动CBM+优化模型。最后，以工业物联网驱动的液压系统为例，对所提出的维护策略进行了验证。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Advanced Engineering Informatics 工程技术-工程：综合

CiteScore

12.40

自引率

18.20%

发文量

292

审稿时长

45 days

期刊介绍： Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.