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论文:2022,Vol:40,Issue(3):645-650 |
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引用本文： |
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池程芝, 潘震, 徐钊, 张一童. 基于多特征选择算法的功率变换器故障分类方法[J]. 西北工业大学学报 |
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CHI Chengzhi, PAN Zhen, XU Zhao, ZHANG Yitong. Power converter fault classification method based on multi-feature selection algorithm[J]. Northwestern polytechnical university |
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| 基于多特征选择算法的功率变换器故障分类方法 |
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| 池程芝1, 潘震1, 徐钊2, 张一童2 |
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1. 中国航空无线电电子研究所 航空电子系统综合技术重点实验室, 上海 200233;
2. 西北工业大学 电子信息学院, 陕西 西安 710072 |
| 摘要: |
| 对综合模块化航电电源转换模块的核心部件进行故障诊断的过程中,选择合适的特征能够有效提高模型的效率和分类准确率,极大地降低学习算法的计算复杂度。设计了典型的Sepic结构DC-DC变换器模型,对DC-DC变换器的典型故障类型进行故障模拟;通过仿真获取相应的原始数据,采用数据进行预处理、特征提取与多特征选择融合;利用BP神经网络方法对DC-DC变换器进行故障诊断分析,仿真验证了该方法的有效性。 |
| 关键词:
特征选择
BP神经网络
故障诊断
功率变换器
Sepic结构
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| Power converter fault classification method based on multi-feature selection algorithm |
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| CHI Chengzhi1, PAN Zhen1, XU Zhao2, ZHANG Yitong2 |
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1. Science and Technology on Avionics Integration Laboratory, China Institute of Aeronautical Radio Electronics, Shanghai 200233, China;
2. School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China |
| Abstract: |
| In the process of fault diagnosis for the core components of the integrated modular avionics power conversion module, selecting appropriate features can effectively improve the efficiency and classification accuracy of the model, and greatly reduce the computational complexity of the learning algorithm. This paper first designs a typical Sepic structure DC-DC converter model to simulate the typical fault types of the DC-DC converter; secondly, the corresponding original data is obtained through simulation; after data preprocessing, feature extraction and using multiple feature selection fusion algorithm, BP neural network method is used finally for fault diagnosis analysis of DC-DC converter. The simulation verifies the effectiveness of the above method. |
| Key words:
feature selection
BP neural network
fault diagnosis
power converter
Sepic structure
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| 收稿日期: 2021-09-03
修回日期:
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| DOI: 10.1051/jnwpu/20224030645 |
| 基金项目: 航空自然科学基金(20185553034)、国防基础科研计划(JCKY2019205C615007)、中国博士后科学基金(2018M633574)及国家自然科学基金(61803309,61603303)资助 |
| 通讯作者: 徐钊(1982—),女,西北工业大学副教授,主要从事寿命预测与健康管理、多无人机网络控制研究。e-mail:zhaoxu@nwpu.edu.cn
Email：zhaoxu@nwpu.edu.cn |
| 作者简介: 池程芝(1984—),中国航空无线电电子研究所高级工程师,主要从事预测与健康管理、航电综合研究。
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| 作者相关文章 |
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| 池程芝 在本刊中的所有文章 |
| 潘震 在本刊中的所有文章 |
| 徐钊 在本刊中的所有文章 |
| 张一童 在本刊中的所有文章 |
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参考文献: |
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[1] 文成林, 吕菲亚, 包哲静, 等. 基于数据驱动的微小故障诊断方法综述[J].自动化学报,2016,42(9):1285-1299 WEN Chenglin, LYU Feiya, BAO Zhejing, et al. A review of data-driven micro-fault diagnosis methods[J]. Acta Automatica Sinica, 2016, 42(9): 1285-1299 (in Chinese)
[2] 毛鹏. 飞机航电系统故障分析方法与故障诊断技术探讨[J]. 电子测试, 2020(3): 106-107 MAO Peng. Discussion on fault analysis method and fault diagnosis technology of aircraft avionics system[J]. Electronic Testing, 2020(3): 106-107 (in Chinese)
[3] 赵燕辉. 基于大数据的航电综合分析系统的设计与实现[D]. 成都: 电子科技大学, 2020 ZHAO Yanhui. Design and implementation of avionics comprehensive analysis system based on big data[D]. Chengdu: University of Electronic Science and Technology of China, 2020 (in Chinese)
[4] 唐贵基, 王晓龙. 参数优化变分模态分解方法在滚动轴承早期故障诊断中的应用[J]. 西安交通大学学报,2015,49(5): 73-81 TANG Guiji, WANG Xiaolong. Application of variational mode decomposition method for parameter optimization in early fault diagnosis of rolling bearings[J]. Journal of Xi'an Jiaotong University, 2015, 49(5): 73-81 (in Chinese)
[5] 赵光权, 葛强强, 刘小勇, 等. 基于DBN的故障特征提取及诊断方法研究[J]. 仪器仪表学报, 2016, 37(9): 1946-1953 ZHAO Guangquan, GE Qiangqiang, LIU Xiaoyong, et al. Research on fault feature extraction and diagnosis method based on DBN[J]. Journal of Instrumentation, 2016, 37(9): 1946-1953 (in Chinese)
[6] 秦琪. 航电电路模块PHM技术研究[D]. 西安: 西北工业大学,2018 QIN Qi. Research on PHM technology of avionics circuit module[D]. Xi'an: Northwestern Polytechnical University, 2018 (in Chinese)
[7] 曾易寒. 基于输出电压特征的DC-DC变换器故障识别与寿命预测方法研究[D]. 成都:电子科技大学, 2020 ZENG Yihan. Research on fault identification and life prediction method of DC-DC converter based on output voltage characteristics[D]. Chengdu: University of Electronic Science and Technology of China, 2020 (in Chinese)
[8] 李运富. 飞机二次电源故障模式分析与故障特征提取[D]. 天津: 中国民航大学, 2020 LI Yunfu. Failure Mode analysis and fault feature extraction of aircraft secondary power supply[D]. Tianjin: Civil Aviation University of China, 2020 (in Chinese)
[9] ZOU Fengqian, ZHANG Haifeng, SANG Shengtian, et al. An anti-noise one-dimension convolutional neural network learning model applying on bearing fault diagnosis[J]. Measurement,2021,186: 110236 |
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1．郭阳明, 冉从宝, 姬昕禹, 马捷中.基于组合优化BP神经网络的模拟电路故障诊断[J]. 西北工业大学学报, 2013,31(1): 44-48 |
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