Zhong Dong, Xia Zhelei, Zhu Yian, Duan Junhua
School of Computer Science, Northwestern Polytechnical University, Xi'an, 710129, China.
Heliyon. 2023 Mar 13;9(4):e14534. doi: 10.1016/j.heliyon.2023.e14534. eCollection 2023 Apr.
The upgrade and development of manufacturing industry makes predictive maintenance more and more important, but the traditional predictive maintenance can not meet the development needs in many cases. In recent years, predictive maintenance based on digital twin has become a research hotspot in the manufacturing industry field. Firstly, this paper introduces the general methods of digital twin technology and predictive maintenance technology, analyzes the gap between them, and points out the importance of using digital twin technology to realize predictive maintenance. Secondly, this paper introduces the predictive maintenance method based on digital twin (PdMDT), introduces its characteristics, and gives its differences from traditional predictive maintenance. Thirdly, this paper introduces the application of this method in intelligent manufacturing, power industry, construction industry, aerospace industry, shipbuilding industry, and summarizes the latest development in these fields. Finally, the PdMDT puts forwards a reference framework in manufacturing industry, the framework describes the specific implementation process of equipment maintenance, and gives an example of industrial robot using the framework, and discusses the limitations, challenges and opportunities of the PdMDT.
制造业的升级与发展使预测性维护变得越来越重要,但传统的预测性维护在很多情况下无法满足发展需求。近年来,基于数字孪生的预测性维护已成为制造业领域的研究热点。首先,本文介绍了数字孪生技术和预测性维护技术的一般方法,分析了它们之间的差距,并指出利用数字孪生技术实现预测性维护的重要性。其次,本文介绍了基于数字孪生的预测性维护方法(PdMDT),介绍了其特点,并给出了它与传统预测性维护的区别。第三,本文介绍了该方法在智能制造、电力行业、建筑业、航空航天业、造船业中的应用,并总结了这些领域的最新发展。最后,PdMDT在制造业中提出了一个参考框架,该框架描述了设备维护的具体实施过程,并给出了一个使用该框架的工业机器人实例,同时讨论了PdMDT的局限性、挑战和机遇。
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