基于分数阶傅里叶变换与卷积神经网络的工业过程故障检测
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沈阳化工大学信息工程学院 沈阳 110142

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TP277

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国家自然科学基金(62273242)项目资助


Fault detection of industrial processes based on fractional order Fourier transform and convolutional neural network
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College of Information Engineering, Shenyang University of Chemical Technology,Shenyang 110142,China

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    摘要:

    基于传统数据驱动的过程故障检测存在忽略正常数据与故障数据之间微小差异和检测不灵敏问题,本文提出了一种基于FRFT和CNN结合的故障检测方法。从放大正常数据与故障数据之间的微小差异方面入手,一则利用CVDA构造残差矩阵用于数据监测,增强灵敏度;二则利用FRFT对数据进行变换,将一些幅值低,易被噪声掩盖的故障从时域转换为频域,尽可能放大其特征,使其易检测。最后利用CNN对处理完的数据进行检测,解决了忽略微小差异和检测灵敏度低的问题,通过TE过程进行实验验证,在故障检测率方面得到提高,表明所提方法的有效性。

    Abstract:

    Based on the problem of ignoring the slight difference between normal data and fault data and insensitive detection of traditional data-driven process fault detection, this paper proposes a fault detection method based on the combination of FRFT and CNN. Starting from amplifying the small differences between normal data and fault data, a residual matrix is constructed by CVDA for data monitoring to enhance sensitivity. The second is to use FRFT to transform the data, convert some faults with low amplitude and easy to be masked by noise from the time domain to the frequency domain, and amplify their characteristics as much as possible to make them easy to detect. Finally, CNN is used to detect the processed data, which solves the problems of ignoring small differences and low detection sensitivity, and experiments are verified by TE process, which improves the fault detection rate and shows the effectiveness of the proposed method.

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李元,辛梦媛.基于分数阶傅里叶变换与卷积神经网络的工业过程故障检测[J].电子测量技术,2024,47(2):1-8

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  • 在线发布日期: 2024-04-30
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