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Multifeatures Fusion and Nonlinear Dimension Reduction for Intelligent Bearing Condition Monitoring

机译：用于智能轴承状态监测的多特征融合和非线性降维

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摘要

Condition-based maintenance is critical to reduce the costs of maintenance and improve the production efficiency. Data-driven method based on neural network (NN) is one of the most used models for mechanical components condition recognition. In this paper, we introduce a new bearing condition recognition method based on multifeatures extraction and deep neural network (DNN). First, the method calculates time domain, frequency domain, and time-frequency domain features to represent characteristic of vibration signals. Then the nonlinear dimension reduction algorithm based on deep learning is proposed to reduce the redundancy information. Finally, the top-layer classifier of deep neural network outputs the bearing condition. The proposed method is validated using experiment test-bed bearing vibration data. Meanwhile some comparative studies are performed; the results show the advantage of the proposed method in adaptive features selection and superior accuracy in bearing condition recognition.
机译：基于状态的维护对于降低维护成本和提高生产效率至关重要。基于神经网络（NN）的数据驱动方法是机械零件状态识别最常用的模型之一。本文介绍了一种基于多特征提取和深度神经网络（DNN）的轴承状态识别新方法。首先，该方法计算时域，频域和时频域特征以表示振动信号的特征。然后提出了一种基于深度学习的非线性降维算法，以减少冗余信息。最后，深度神经网络的顶层分类器输出轴承状况。利用试验台轴承振动数据验证了该方法的有效性。同时进行了一些比较研究。结果表明，该方法具有自适应特征选择的优势，在轴承状态识别中具有较高的精度。

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  • 来源
    《Shock and vibration》 |2016年第3期|4632562.1-4632562.10|共10页
  • 作者

    Guo Liang; Gao Hongli; Huang Haifeng; He Xiang; Li ShiChao;

  • 作者单位

    Southwest Jiaotong Univ, Sch Mech Engn, Chengdu 610031, Peoples R China;

    Southwest Jiaotong Univ, Sch Mech Engn, Chengdu 610031, Peoples R China;

    Southwest Jiaotong Univ, Sch Mech Engn, Chengdu 610031, Peoples R China;

    Southwest Jiaotong Univ, Sch Mech Engn, Chengdu 610031, Peoples R China;

    Southwest Jiaotong Univ, Sch Mech Engn, Chengdu 610031, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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