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首页>《中国测试》期刊>本期导读>基于SDA和KPCA特征融合的供输弹系统早期故障识别

基于SDA和KPCA特征融合的供输弹系统早期故障识别

83    2019-04-28

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作者:梁海英, 许昕, 潘宏侠, 付志敏, 张航

作者单位:中北大学机械工程学院, 山西 太原 030051


关键词:供输弹系统;堆栈降噪自动编码器;核主成分分析;信息融合;故障识别


摘要:

对于供输弹系统早期故障信号成分复杂,潜在故障征兆难以识别的问题,提出基于堆叠式降噪自动编码器(SDA)和核主成分分析(KPCA)特征融合的早期故障识别方法。所采集的供输弹系统信号经过去趋势项和五点三次平滑法处理后,首先将不同状态的振动信号和声压信号分别通过SDA进行特征提取;然后用KPCA对提取的振动信号和声压信号特征进行融合;最后运用支持向量机(SVM)对融合前后的特征分别进行识别并对比。试验结果表明,该方法能有效地对供输弹系统早期故障进行识别,且识别准确率达92.4%。


Early fault identification of ammunition supply system based on SDA and KPCA feature fusion
LIANG Haiying, XU Xin, PAN Hongxia, FU Zhimin, ZHANG Hang
Mechanical Engineering Institute, North University of China, Taiyuan 030051, China
Abstract: For the problem that the early fault signal components of the ammunition supply system are complicated and the potential fault signs are difficult to identify, an early fault identification method based on SDA and KPCA feature fusion is proposed. After the collected signal for the ramming system is processed by the detrending term and the five-point three-smoothing method, the vibration signals and the acoustic signals of different states are first extracted by SDA respectively.Then, KPCA is used to fuse the extracted vibration signal and sound pressure signal features. Finally, the SVM is used to classify and compare the features before and after fusion. The test results show that the method can effectively identify the early failure of the ammunition supply system and the recognition accuracy is 92.4%.
Keywords: ammunition supply system;SDA;KPCA;information fusion;fault identification
2019, 45(4):141-145,150  收稿日期: 2018-07-24;收到修改稿日期: 2018-09-01
基金项目: 国家自然科学基金资助项目(51675491)
作者简介: 梁海英(1993-),女,河北张家口市人,硕士研究生,专业方向为信号识别与处理、装备系统检测与诊断
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