煤炭工程 ›› 2015, Vol. 47 ›› Issue (1): 101-103.doi: 10.11799/ce201501032
• 研究探讨 • 上一篇 下一篇
吕振江1,汤家升2
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摘要： 针对煤矿风机系统振动故障的复杂性，提出了一种基于遗传算法优化BP神经网络〖DK〗(GA-BP〖DK〗)的故障诊断方法。依据归一化的故障特征量样本和目标期望输出，对诊断网络进行了达标训练。通过验证数据进行网络诊断测试，证明该方法可以满足风机故障诊断的快速性和准确性。
关键词: 关键词：风机系统, 故障诊断, 遗传算法, BP神经网络
Abstract: Abstract: Aimed at the complexity of the fan unit fault, a fault diagnosis method based on the BP neural network optimized by the genetic algorithm (GA-BP) is adopted. According to the normalized failure characteristics samples and desired output, the diagnosis network is trained for the standard goal. By testing the validation data through the trained net, the results show that the method meets the requirements of speed and accuracy in diagnosis and has practical application value. The paper lays the foundation for bettering fan vibration fault diagnosis technology.
Key words: Key words: fan unit, fault diagnosis, genetic algorithm, BPneural network
中图分类号:
TD655
吕振江，汤家升. 基于遗传算法的风机故障诊断研究[J]. 煤炭工程, 2015, 47(1): 101-103.
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