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作 者:李志明 Li Zhiming(Shanxi Fenhe Coking Coal Co.,Ltd.,Hongtong Shanxi 041600,China)
机构地区:[1]山西汾河焦煤股份有限公司,山西洪洞041600
出 处:《机械管理开发》2025年第3期210-212,共3页Mechanical Management and Development
摘 要:为进一步提升煤矿带式输送机皮带损伤检测能力,以保障设备安全运行,以机器学习技术为基础构建煤矿带式输送机皮带损伤检测系统架构,并对该系统中的图像处理、数据处理和皮带损伤类型检测三项关键功能的模型架构与算法流程进行详细设计,以完成系统设计。从试验测试结果来看,此系统基本能够实现对皮带损伤的实时准确检测,具有潜在应用价值。In order to further improve the belt damage detection capability of coal mine belt conveyor and ensure the safe operation of equipment,the structure of the belt damage detection system of coal mine belt conveyor was built based on machine learning technology,and the model architecture and algorithm flow of the three key functions of the system,including image processing,data processing and belt damage type detection,were designed in detail to complete the system design.From the experimental test results,the system can basically realize real-time and accurate detection of belt damage,and has potential application value.
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