Current research on Digital Twin(DT)based Prognostics and Health Management(PHM)focuses on establishment of DT through integration of real-time data from various sources to facilitate comprehensive product monitoring and health management.However,there still exist gaps in the seamless integration of DT and PHM,as well as in the development of DT multi-field coupling modeling and its dynamic update mechanism.When the product experiences long-period degradation under load spectrum,it is challenging to describe the dynamic evolution of the health status and degradation progression accurately.In addition,DT update algorithms are difficult to be integrated simultaneously by current methods.This paper proposes an innovative dual loop DT based PHM framework,in which the first loop establishes the basic dynamic DT with multi-filed coupling,and the second loop implements the PHM and the abnormal detection to provide the interaction between the dual loops through updating mechanism.The proposed method pays attention to the internal state changes with degradation and interactive mapping with dynamic parameter updating.Furthermore,the Independence Principle for the abnormal detection is proposed to refine the theory of DT.Events at the first loop focus on accurate modeling of multi-field coupling,while the events at the second loop focus on real-time occurrence of anomalies and the product degradation trend.The interaction and collaboration between different loop models are also discussed.Finally,the Permanent Magnet Synchronous Motor(PMSM)is used to verify the proposed method.The results show that the modeling method proposed can accurately track the lifecycle performance changes of the entity and carry out remaining life prediction and health management effectively.
针对当前高密度的航天发射任务和新域新质战斗力建设的高要求,传统的故障预测与健康管理已无法满足航天测量设备的维护需求。结合新体制的软件化雷遥一体测量设备的研制,本文提出了数字孪生技术支持下的设备故障预测和健康管理设计框架。通过结合雷遥一体设备的复杂结构组成,梳理出构建其数字孪生体模型的关键细节,分析软件化雷遥一体设备的PHM功能需求,以此提出了五层的数字孪生系统架构,并给出了实现PHM服务的具体运行机制。该设计框架为提高新体制软件化雷遥一体设备的全周期运行的可靠性提供了初步方案,同时也为其他典型的装备维护提供了一定的技术支持和理论参考。The traditional prognostics and health management can no longer meet the maintenance requirements of space measuring equipment under the high density of space launch missions and the high requirements of new field and quality combat capability construction. In combination with the development of a new system of software-based radar & telemetry integrated equipment, this paper proposes a design framework of equipment prognostics and health management supported by digital twin technology. By combining the complex structural composition of the radar & telemetry integrated equipment, the key details of constructing its digital twin model are carded out, and the PHM functional requirements of the software-based radar & telemetry integrated equipment are analyzed. Based on this, a five-layer digital twin system architecture is proposed, and the specific operation mechanism for implementing PHM services is provided. The design framework provides a preliminary scheme to improve the reliability of the whole cycle operation of the new system of software-based radar & telemetry integrated equipment, and also provides some technical support and theoretical reference for other typical equipment maintenance.
As the fundamental and key technique to ensure the safe and reliable operation of vital systems,prognostics with an emphasis on the remaining useful life(RUL)prediction has attracted great attention in the last decades.In this paper,we briefly discuss the general idea and advances of various prognostics and RUL prediction methods for machinery,mainly including data-driven methods,physics-based methods,hybrid methods,etc.Based on the observations fromthe state of the art,we provide comprehensive discussions on the possible opportunities and challenges of prognostics and RUL prediction of machinery so as to steer the future development.
The prognostics health management(PHM)fromthe systematic viewis critical to the healthy continuous operation of processmanufacturing systems(PMS),with different kinds of dynamic interference events.This paper proposes a three leveled digital twinmodel for the systematic PHMof PMSs.The unit-leveled digital twinmodel of each basic device unit of PMSs is constructed based on edge computing,which can provide real-time monitoring and analysis of the device status.The station-leveled digital twin models in the PMSs are designed to optimize and control the process parameters,which are deployed for the manufacturing execution on the fog server.The shop-leveled digital twin maintenancemodel is designed for production planning,which gives production instructions fromthe private industrial cloud server.To cope with the dynamic disturbances of a PMS,a big data-driven framework is proposed to control the three-level digital twin models,which contains indicator prediction,influence evaluation,and decisionmaking.Finally,a case study with a real chemical fiber system is introduced to illustrate the effectiveness of the digital twin model with edge-fog-cloud computing for the systematic PHM of PMSs.The result demonstrates that the three-leveled digital twin model for the systematic PHM in PMSs works well in the system’s respects.
Jie RenChuqiao XuJunliang WangJie ZhangXinhua MaoWei Shen
Scientific research frequently involves the use of computational tools and methods.Providing thorough documentation,open-source code,and data–the creation of reproducible computational research(RCR)–helps others understand a researcher’s work.In this study,we investigate the state of reproducible computational research,broadly,and from within the field of prognostics and health management(PHM).In a text mining survey of more than 300 articles,we show that fewer than 1%of PHM researchers make their code and data available to others.To promote the RCR further,our work also highlights several personal benefits for those engaged in the practice.Finally,we introduce an open-source software tool,called PyPHM,to assist PHM researchers in accessing and preprocessing common industrial datasets.
