Overview of predictive maintenance based on digital twin technology.

Area of Science:

• Manufacturing Engineering
• Industrial Informatics
• Maintenance Engineering

Background:

• The rapid evolution of the manufacturing industry necessitates advanced predictive maintenance (PdM) strategies.
• Traditional PdM methods often fall short of meeting the demands of modern industrial development.
• Predictive maintenance leveraging digital twin technology has emerged as a significant research focus.

Purpose of the Study:

• To explore the integration of digital twin technology with predictive maintenance.
• To introduce and differentiate predictive maintenance based on digital twin (PdMDT) from conventional approaches.
• To present a framework for PdMDT implementation in manufacturing.

Main Methods:

• Review of digital twin and predictive maintenance methodologies.
• Analysis of the synergy and gaps between digital twin and PdM.
• Introduction of the PdMDT concept, its features, and comparative study with traditional PdM.
• Case studies of PdMDT application across various industries.

Main Results:

• Digital twin technology significantly enhances the capabilities of predictive maintenance.
• PdMDT offers distinct advantages over traditional predictive maintenance methods.
• Successful applications of PdMDT are demonstrated in intelligent manufacturing, power, construction, aerospace, and shipbuilding sectors.
• A reference framework for PdMDT implementation in manufacturing, with an industrial robot example, is proposed.

Conclusions:

• PdMDT represents a crucial advancement for modern manufacturing maintenance.
• The proposed framework provides a practical approach for implementing PdMDT.
• Further research is needed to address the limitations and challenges while capitalizing on the opportunities of PdMDT.