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    • 基于深度置信网络的轴承故障识别分析与研究

    LIU Yuxuan, WANG Lin, ZHANG Pengzhen, XU Xin,YIN Xiaowei,CHEN Jichi

    Journal of Shenyang Institute of Engineering(Natural Science)（2023）

    沈阳工程学院

    Cited 0|Views10
    Abstract
    轴承为诸多机械设备的重要零部件,对其故障状态的识别对于设备的稳定运行具有重要的意义.本文首先利用改进的自适应噪声完全集合经验模态分解(ICEEMDAN)与小波阈值相结合的方法去除轴承振动信号中的伪迹,然后分别提取信号的标准差、峭度、样本熵等线性和非线性特征,最后将多域特征作为输入项,利用深度置信网络(DBN)进行训练识别,建立了能够有效识别轴承故障类型的网络模型.试验结果表明:该模型对轴承故障类型识别的正确率可达97.8%.
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    Key words
    Bearing vibration signal,ICEEMDAN,wavelet threshold,deep belief network
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