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基于萤火虫算法和LabCVI的煤矿机械液压系统故障诊断研究 被引量:2

Research on Fault Diagnosis of Mine Machinery Hydraulic System Based on FA and LabCVI
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摘要 由于煤矿机械液压设备工作环境复杂,因而容易出现各类故障,为了保证煤矿机械液压设备的正常工作,需要对液压系统故障进行快速在线诊断。提出了一种基于改进萤火虫算法和LabCVI的煤矿机械液压故障诊断系统,使用Wine和Heart数据集进行了验证,对常见的煤矿机械液压设备的故障进行了分析和总结,阐述了机械液压故障诊断系统的整体结构,设计了煤矿机械液压设备故障诊断流程,并使用训练样本和验证样本分别对系统进行了训练和测试,测试结果表明系统可以满足煤矿液压机械设备故障诊断的需求。 Because the coal mine machinery hydraulic equipment work environment is complex, therefore easy to appear each kind of breakdown, in order to ensure the normal operation of coal mine machinery hydraulic equipment, it is necessary to carry out fast on-line fault diagnosis of hydraulic system. A hydraulic fault diagnosis system for coal mine machinery based on improved Firefly algorithm and LabCVI was proposed and validated with Wine and Heart datasets, analyzes and summarizes the common faults of the coal mine machinery hydraulic equipment, expounds the whole structure of the mechanical hydraulic fault diagnosis system, and designs the fault diagnosis flow of the coal mine machinery hydraulic equipment, the system is trained and tested with training samples and test samples respectively. The test results show that the system can meet the needs of fault diagnosis of coal mine hydraulic machinery equipment.
作者 王保军 吕玉兰 WANG Baojun;LYU Yulan(Shanxi Institute of Mechanical and Electrical Engineering,Changzhi 046011,China)
出处 《煤炭技术》 CAS 北大核心 2023年第10期224-227,共4页 Coal Technology
基金 2022年度山西省高等学校科技创新项目(2022L686) 山西机电职业技术学院科技创新类课题(KT-20013)。
关键词 萤火虫算法 LabCVI 煤矿 液压设备 神经网络 FA LabCVI coal mine hydraulic equipment neural network
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