Digital Twin in Prognostics and Health Management Era

Dear Colleagues,

PHM uses sensors to monitor the states of devices in real time, uses various models and algorithms to perform fault diagnosis, fault prognostics, and remaining life prediction, and creates the optimal maintenance plan. Digital twin is an essential technology for PHM. Digital twin refers to the process and method of using digital technology to describe and model the characteristics, behavior, process, and performance of physical objects. The combination of digital twin and prognostics and health management (PHM) holds immense potential for innovation and application. This Special Issue aims to illuminate the  cutting-edge research in digital twin technology for PHM.

The convergence of digital twin and PHM in the context of Industry 4.0 offers novel avenues for optimizing system performance, predictive maintenance, and efficient operations. This Special Issue invites original research, comprehensive reviews, and case studies that explore this symbiotic interaction, fostering insights into their combined impact on technology and industry.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Integration of digital twin and PHM methodologies in CPSs;
• Applications of digital twin and PHM in smart manufacturing;
• Real-time monitoring and predictive maintenance using digital twin;
• Data analytics and AI techniques for enhancing PHM through digital twin;
• Security considerations in implementing digital twin and PHM in Industry 4.0;
• Economic and environmental implications of combined digital twin–PHM strategies;
• Human–machine interaction and user-centered design for digital-twin-ehanced PHM studies;
• Challenges and opportunities of synergizing digital twin and PHM.

We look forward to receiving your contributions.

Prof. Dr. Yu Zheng
Prof. Dr. Jinsong Bao
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• digital twin
• PHM (prognostics and health management)
• CPS
• Industry 4.0
• smart manufacturing