| 李 晟,邓江云,周兴龙,李玉晓,徐飞洋.基于 LSTM 的电子系统间歇故障严重程度识别方法[J].电子测量与仪器学报,2022,36(3):139-148 |
| 基于 LSTM 的电子系统间歇故障严重程度识别方法 |
| Intermittent fault severity recognition method forelectronic systems based on LSTM |
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| DOI： |
| 中文关键词: 长短期记忆 间歇故障 电子系统 故障严重程度 故障注入 |
| 英文关键词:long short-term memory(LSTM) intermittent fault electronic system fault severity fault injection |
| 基金项目:国家自然科学基金（11875149，61565007，61762047）项目资助 |
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| 中文摘要: |
| 间歇故障的累积会导致电子系统健康状态退化,正确识别电子系统间歇故障严重程度是保障系统安全运行、降低维护
成本的关键。 针对间歇故障特征难以准确提取导致传统识别方法失效的问题。 本文提出了一种基于长短期记忆(LSTM)网络
的间歇故障严重程度识别方法,首先将间歇故障注入电子系统获取足量不同严重程度的训练数据。 再用这些数据训练由 LSTM
网络与 softmax 全连接层网络构建的严重程度识别模型。 最后,通过对典型电路的故障注入,使用训练好的 LSTM 网络对间歇
故障严重程度进行识别,实验结果证明了方法的有效性和可行性。 |
| 英文摘要: |
| The accumulation of intermittent faults will cause the deterioration of the health of the electronic system. Correctly identifying
the severity of intermittent faults can ensure the safe operation and reduce maintenance costs of the electrical systems. However, it is
difficult to extract intermittent fault features accurately, which leads to the failure of traditional identification methods. This paper
proposes a method for identifying the severity of intermittent faults based on LSTM network. First, the intermittent faults are injected into
the electronic system to obtain sufficient training data of different severity. Then use these data to train the classifier which is constructed
by LSTM network and the softmax fully connected layer network. Finally, by injecting faults into typical circuits and using the trained
LSTM network to identify the severity of intermittent faults, the experimental results prove the effectiveness and feasibility of the method. |
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