A fault diagnosis system of mine main ventilator
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摘要: 采用经极限学习机训练的神经网络建立故障诊断模型,基于该模型设计了一种矿井主要通风机故障诊断系统,介绍了该系统的软硬件设计方案。测试结果表明,该系统中极限学习机算法运行时间仅为0.031 3 s,故障诊断准确率不低于97.35%,其实时性和准确性优于基于BP神经网络、ELMAN神经网络、经支持向量机训练的神经网络等模型的主要通风机故障诊断系统。Abstract: A fault diagnosis model was built by use of neural network trained by extreme learning machine. A fault diagnosis system of mine main ventilator based on the model was designed, and software and hardware design schemes of the system were introduced. The test results show running time of extreme learning machine algorithm in the system is only 0.031 3 s and accuracy rate of fault diagnosis is not less than 97.35%, which has better real-time performance and accuracy than fault diagnosis systems based on BP neural network, ELMAN neural network or neural network trained by support vector machine.
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Keywords:
- coal mining /
- main ventilator /
- fault monitoring /
- fault diagnosis /
- extreme learning machine
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期刊类型引用(3)
1. 张金燕. 超声相控阵技术在液压支架无损检测中的应用. 自动化应用. 2024(01): 153-154+158 . 百度学术
2. 陈泽坤. 煤矿综采液压支架常见故障和对策. 内蒙古煤炭经济. 2024(10): 171-173 . 百度学术
3. 薄园. 大采高液压支架整机性能分析. 自动化应用. 2023(21): 122-123+126 . 百度学术
其他类型引用(1)
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