A Method for Evaluating the Severity of Intermittent Faults of Electronic Systems Based on Variational Mode Decomposition-Gated Recurrent Units
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摘要: 针对电子系统间歇故障信号受噪声影响大且冗余信息多，导致深度神经网络模型对间歇故障严重程度评估能力受限的问题，该文提出一种基于变分模式分解和门循环单元(VMD-GRU)的间歇故障严重程度评估方法。先通过变分模式分解(VMD)对间歇故障信号进行自适应分解得到所有固有模式函数(IMF)分量，再对IMF分量进行相似度分析选择敏感分量，并利用微分增强型能量算子构建严重程度敏感因子。最后，利用严重程度敏感因子训练门循环单元(GRU)循环神经网络评估模型。通过对电子系统的关键电路注入不同严重程度的间歇故障进行评估，结果表明该方法有较强的间歇故障严重程度评估能力，评估结果更加准确有效。
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关键词:
- 间歇故障 /
- 严重程度敏感因子 /
- 变分模式分解和门循环单元 /
- 故障注入 /
- 电子系统
Abstract: Considering the problem that the intermittent fault signal of the electronic system is greatly affected by noise and has a lot of redundant information, which results in the limitation of the deep neural network model to evaluate the severity of the intermittent fault. A method for evaluating the severity of intermittent faults based on Variational Mode Decomposition-Gated Recurrent Units (VMD-GRU) is proposed. Firstly, all Intrinsic Mode Function (IMF) components are adaptively decomposed on intermittent fault signals through Variational Mode Decomposition (VMD). Then the sensitivity analysis of the IMF components is performed to select the sensitive components, and the differential enhanced energy operator is used to construct the severity sensitivity factor. Finally, the severity sensitivity factor is used to train the Gated Recurrent Units (GRU) recurrent neural network severity evaluation model. Through the evaluation of intermittent faults of different severity injected into the key circuits of electronic systems, the results show that this method has a strong ability to evaluate the severity of intermittent faults, and is more accurate and effective in evaluating the severity of intermittent faults. -
表 1 模型基本参数设置
模型参数 数值 信号输入长度 20 GRU隐藏层数 200 全连接层神经元数 5 初始学习率 0.001 学习率下降系数 0.5 学习率下降周期 20 批量大小 20 最大周期数 200 
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