Fan fault simulation and diagnosis training system and method

By integrating technologies such as virtual reality, artificial intelligence, quantum computing and digital twins, a highly realistic fan operation training environment is created, which solves the problem that existing systems cannot truly reproduce fan operating conditions and failure scenarios, and significantly improves training results and operational efficiency.

CN119723977APending Publication Date: 2025-03-28PENGLAI WIND POWER BRANCH OF HUANENG SHANDONG POWER GENERATION CO LTD +1
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Patent Information

Application Number
CN202411848512.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-16
Publication Date
2025-03-28

AI Technical Summary

Technical Problem

The existing fan fault simulation and diagnosis training system cannot fully and truly reproduce complex fan operating conditions and failure scenarios, making it difficult for operators to make quick and accurate judgments and decisions when facing real failures.

Method used

Using virtual reality simulation environment module, multi-modal sensor fusion module, fault generation and diagnosis module, quantum augmented prediction and optimization module, adaptive learning and evaluation module and multi-scale digital twin platform, a highly realistic fan operation training environment is created by integrating virtual reality, artificial intelligence, quantum computing and digital twin technologies.

Benefits of technology

It significantly improves the practicality and effectiveness of training, enhances the accuracy of fault simulation and the reliability of diagnosis, helps operators better understand and master the diagnosis and treatment methods of fan failures, and improves the operational efficiency and safety of wind farms.

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Abstract

The invention discloses a fan fault simulation and diagnosis training system and method. A plurality of modules are integrated to realize comprehensive fan fault simulation, diagnosis and training functions. The virtual reality simulation environment module is responsible for creating a fan three-dimensional model and an operation environment and collecting operation data of students; the multi-modal sensor fusion module is used for collecting multi-dimensional data of the running state of the fan; the fault generation and diagnosis module receives the data, generates a simulation fault scene, performs intelligent diagnosis, and feeds back a result to the virtual reality simulation environment module for student reference; the quantum enhancement prediction and optimization module carries out fault prediction according to historical and real-time data of fan operation and provides optimization suggestions; the self-adaptive learning and evaluation module dynamically adjusts the training content and difficulty according to the performance of the trainee; the multi-scale digital twin platform simulates a fan system in an omnibearing manner and shares data with each module; the system provides a vivid training environment for trainees, and is helpful for improving fan fault processing skills.
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