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基于神经网络的轴承故障预测模型 被引量：6

Prognostics Model of Bearing Fault Based on Neural Networks
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摘要 利用在故障预测领域广泛应用的神经网络模型,对轴承监测数据的特征提取与建模,挖掘出监测数据与剩余寿命间内在关联,从而对轴承剩余寿命做出评估。在轴承全寿命数据的实际实验中,证实了该模型的有效性。 In this paper, neural networks were used to estimate the remaining useful life of the bearings. Through the feature extraction and modeling process, the intrinsic connection between monitoring data and remaining useful life was dig out, so as to evaluate the residual life of bearing. In the experiment, the effectiveness of the model was verified.
作者 韩昕锋 任立坤
机构地区 海军装备部装备采购中心 海军航空工程学院
出处 《海军航空工程学院学报》 2015年第3期281-285,共5页 Journal of Naval Aeronautical and Astronautical University
关键词 故障预测 神经网络 状态监测 fault prognostics neural networks state monitoring
分类号 TB115.2 [理学—应用数学]
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参考文献21

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二级参考文献7

  • 1吴欣,郭创新,曹一家.基于贝叶斯网络及信息时序属性的电力系统故障诊断方法[J].中国电机工程学报,2005,25(13):14-18. 被引量：84
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共引文献19

同被引文献39

引证文献6

二级引证文献51

海军航空工程学院学报
2015年 第3期

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