Dynamic health degree evaluation and predictive maintenance method for power equipment

By constructing parameterized digital twins of power equipment and using a multi-model collaborative approach, the problem of data and model separation was solved, enabling dynamic assessment and predictive maintenance of equipment health status, thereby improving operation and maintenance efficiency and equipment reliability.

CN121599644APending Publication Date: 2026-03-03NANJING HENGXING AUTOMATION EQUIP
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Patent Information

Application Number
CN202511671733.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-14
Publication Date
2026-03-03

AI Technical Summary

Technical Problem

In existing technologies, the data of power equipment lacks deep correlation with the 3D model, the model cannot dynamically reflect the real state, the health assessment capability is insufficient, the construction efficiency is low and it relies on experience, resulting in uneconomical and unsafe maintenance strategies.

Method used

Construct a parameterized digital twin of power equipment, collect various data and calculate health components through a multi-model collaborative method to generate a comprehensive health index, make predictive maintenance decisions, and assist in execution through visualization and augmented reality.

Benefits of technology

It enables precise quantification and predictive maintenance of the health status of power equipment, improving equipment reliability and service life, while reducing maintenance costs and power outage risks.

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Abstract

The invention discloses a power equipment dynamic health degree assessment and predictive maintenance method, and belongs to the technical field of railway power system operation and maintenance. The method comprises the following steps: constructing a parameterized digital twinborn body of power equipment, and collecting real-time operation data, resume data and environment data; based on the parameterized digital twins and the collected data, equipment health degree components are calculated through a multi-model cooperation method, and a comprehensive health index is generated through fusion; performing equipment life prediction and maintenance decision generation according to the comprehensive health index, and outputting an optimal maintenance strategy; and performing visual virtual rehearsal and augmented reality auxiliary execution on the optimal maintenance strategy to form a closed-loop maintenance system. According to the method, the problems of data and model separation, model static stiffness and health assessment deficiency in the prior art are solved, dynamic perception, accurate assessment and predictive maintenance of the equipment state are realized, and the operation and maintenance efficiency and the system reliability are improved.
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