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首页> 外文期刊>Mathematical Problems in Engineering >Fault Identification Method of Diesel Engine in Light of Pearson Correlation Coefficient Diagram and Orthogonal Vibration Signals
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Fault Identification Method of Diesel Engine in Light of Pearson Correlation Coefficient Diagram and Orthogonal Vibration Signals

机译：鉴于Pearson相关系数图和正交振动信号的柴油发动机故障识别方法

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摘要

In order to select fault feature parameters simply and quickly and improve the identification rate of diesel engine faults by using the vibration signals, this paper proposes a diesel engine fault identification method on the basis of the Pearson correlation coefficient diagram (PCC Diagram) and the orthogonal vibration signals. At first, the orthogonal vibration acceleration signals are synchronously acquired in the direction of the top and side of the cylinder head. And the time-domain feature parameters are extracted from the orthogonal vibration acceleration signals to obtain the Pearson correlation coefficient (PCC). Then, the correlation coefficient diagram used to do feature parameter screening is constructed by selecting the feature parameters with the correlation coefficient of more than 0.9. Finally, generalized regression neural network (GRNN) is adopted to classify and identify fuel supply fault in diesel engine. The results show that using the PCC Diagram can simplify the selection process of the feature parameters of the orthogonal vibration signals quickly and effectively. It can also improve the fault identification rate of diesel engine significantly with the help of adding the newly proposed cross-correlation coefficient of the orthogonal vibration signals into the GRNN input feature vector set.
机译：为了简单快速地选择故障特征参数，通过使用振动信号来简单快速地提高柴油发动机故障的识别率，本文基于Pearson相关系数图（PCC图）和正交的柴油发动机故障识别方法提出了柴油发动机故障识别方法振动信号。首先，在气缸盖的顶部和侧的方向上同步地获取正交振动加速信号。并且从正交振动加速度信号中提取时域特征参数以获得Pearson相关系数（PCC）。然后，通过选择具有大于0.9的相关系数的特征参数来构建用于进行特征参数筛选的相关系数图。最后，采用广义回归神经网络（GRNN）进行分类和识别柴油发动机的燃料供应故障。结果表明，使用PCC图可以快速有效地简化正交振动信号的特征参数的选择过程。借助于将正交振动信号的新提出的互相关系数添加到GRNN输入特征向量组中，还可以显着提高柴油发动机的故障识别率。

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  • 来源
    《Mathematical Problems in Engineering》 |2019年第5期|2837580.1-2837580.10|共10页
  • 作者

    Li Zhiyong; Zhao Hongdong; Zeng Ruili; Xia Kewen; Guo Qiang; Li Yuhai;

  • 作者单位

    Hebei Univ Technol Sch Elect & Informat Engn Tianjin 300401 Peoples R China|Army Mil Transportat Univ Dept Basic Sci Tianjin 300161 Peoples R China|Key Lab Elect Informat Control & Secur Technol Tianjin 300308 Peoples R China;

    Hebei Univ Technol Sch Elect & Informat Engn Tianjin 300401 Peoples R China;

    Army Mil Transportat Univ Dept Mil Vehicle Tianjin 300161 Peoples R China;

    Hebei Univ Technol Sch Elect & Informat Engn Tianjin 300401 Peoples R China;

    Army Mil Transportat Univ Dept Basic Sci Tianjin 300161 Peoples R China;

    Key Lab Elect Informat Control & Secur Technol Tianjin 300308 Peoples R China;

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