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内燃机振动时频图像的编码特征提取与诊断

张世雄 蔡艳平 石林锁 牟伟杰

张世雄, 蔡艳平, 石林锁, 牟伟杰. 内燃机振动时频图像的编码特征提取与诊断[J]. 机械科学与技术, 2018, 37(3): 391-395. doi: 10.13433/j.cnki.1003-8728.2018.0310
引用本文: 张世雄, 蔡艳平, 石林锁, 牟伟杰. 内燃机振动时频图像的编码特征提取与诊断[J]. 机械科学与技术, 2018, 37(3): 391-395. doi: 10.13433/j.cnki.1003-8728.2018.0310
Zhang Shixiong, Cai Yanping, Shi Linsuo, Mou Weijie. Coding Feature Extraction and Diagnosis of I.C. Engine Vibration Time-frequency Images[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(3): 391-395. doi: 10.13433/j.cnki.1003-8728.2018.0310
Citation: Zhang Shixiong, Cai Yanping, Shi Linsuo, Mou Weijie. Coding Feature Extraction and Diagnosis of I.C. Engine Vibration Time-frequency Images[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(3): 391-395. doi: 10.13433/j.cnki.1003-8728.2018.0310

内燃机振动时频图像的编码特征提取与诊断

doi: 10.13433/j.cnki.1003-8728.2018.0310
基金项目: 

国家自然科学基金项目(51405498)、陕西省自然科学基金项目(2013JQ8023)及中国博士后基金项目(2015M582642)资助

详细信息
    作者简介:

    张世雄(1992-),硕士研究生,研究方向为机电设备故障诊断与维修,898070012@qq.com

    通讯作者:

    蔡艳平,副教授,博士,caiyanping502@163.com

Coding Feature Extraction and Diagnosis of I.C. Engine Vibration Time-frequency Images

  • 摘要: 针对传统内燃机振动诊断方法在参数选择和特征提取方面的难题,提出一种将S变换和模块二维主成分分析(M-2DPCA)相结合的内燃机故障诊断方法。该方法首先利用S变换将采集到的内燃机缸盖表面振动信号生成振动谱图像;然后通过M-2DPCA对图像矩阵进行模块化处理,利用所有样本子图像构建总体散布矩阵,计算最优投影向量,进行图像特征参数提取;最后,利用最近邻分类器进行分类识别,完成诊断。将该方法应用于内燃机气阀机构8种工况下振动信号的诊断实例中,识别率可达到94.17%,证明了该方法的有效性。
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出版历程
  • 收稿日期:  2017-01-05
  • 刊出日期:  2018-03-05

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