A case intelligent recommendation system based on equipment failure maintenance

By constructing a fault prediction model and simulating the maintenance sequence in a three-dimensional virtual space, the maintenance plan was optimized and adjusted, solving the problem that the existing system failed to consider the complexity of equipment operating conditions, and achieving more efficient and reliable maintenance results.

CN122243468APending Publication Date: 2026-06-19LIAONING MINDI INTELLIGENT IND TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
LIAONING MINDI INTELLIGENT IND TECH CO LTD
Filing Date
2026-03-23
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

Existing fault prediction and maintenance plan recommendation systems fail to fully consider the complexity of equipment operating conditions and the impact of maintenance sequence on effectiveness. This results in recommended plans failing to achieve the expected results in actual implementation and may even cause new problems, thus failing to meet the requirements of modern industrial production for efficiency, accuracy, and reliability in equipment maintenance.

Method used

By constructing a fault prediction model, the current operating condition data of the equipment is obtained, and the best maintenance plan is determined by combining it with historical data. Different maintenance sequences are simulated in a three-dimensional virtual space, and the plan is optimized and adjusted to the final recommended plan to ensure that the plan is adapted to the actual operating conditions and scenario requirements.

Benefits of technology

It improves the applicability and accuracy of maintenance plans, avoids problems caused by improper maintenance sequence, improves maintenance efficiency and reliability, and ensures that the plan can adapt to changes in equipment operating conditions and the needs of different maintenance scenarios.

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Abstract

This invention discloses an intelligent recommendation system for equipment fault repair cases, relating to the field of fault repair technology. It includes a data acquisition unit, a matching and recommendation unit, a simulation verification unit, and an optimization and update unit. The data acquisition unit acquires real-time data on the current operating conditions of the equipment, determines the average lifespan of the equipment based on historical lifespans of the same model, constructs a fault prediction model based on historical operating condition data, predicts fault occurrence points, and sends the predictions to the matching and recommendation unit. It fully considers the impact of repair sequence on repair effectiveness, aiming to find the most suitable repair sequence for actual operating conditions, avoiding new problems caused by improper repair sequences, and improving repair efficiency and reliability. Furthermore, it adjusts and optimizes the repair plan in real-time based on actual verification results, enabling the repair plan to better adapt to changes in equipment operating conditions and the needs of different repair scenarios, thus improving the applicability and accuracy of the repair plan.
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