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高负荷下船舶柴油机滑动轴承位移检测方法研究
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  • 英文篇名:Displacement detection method for sliding bearing of marine diesel engine under high load
  • 作者:程洋
  • 英文作者:CHENG Yang;Faculty of Mechanical Engineering, Nantong Institute of Technology;
  • 关键词:滑动轴承 ; 位移检测 ; 自相关系数 ; 特征聚类 ; 时间窗口 ; 频域特征
  • 英文关键词:sliding bearing;;displacement detection;;autocorrelation coefficient;;feature clustering;;time window;;frequency domain feature
  • 中文刊名:JCKX
  • 英文刊名:Ship Science and Technology
  • 机构:南通理工学院机械工程学院;
  • 出版日期:2018-12-23
  • 出版单位:舰船科学技术
  • 年:2018
  • 期:v.40
  • 基金:“十三五”江苏省一级学科省重点建设学科资助项目(2016-0802);; 江苏高校品牌专业建设工程资助项目(PPZY2015C251)
  • 语种:中文;
  • 页:JCKX201824022
  • 页数:3
  • CN:24
  • ISSN:11-1885/U
  • 分类号:65-67
摘要
现有船舶柴油机滑动轴承位移检测方法存在定位精准性较低、检测灵活性不达标等弊端。为解决上述问题,提出高负荷下的新型船舶柴油机滑动轴承位移检测方法。通过标准化自相关系数确定、位移量特征聚类2个步骤,完成高负荷柴油机滑动轴承位移检测准备。在此基础上,通过滑动检测时间窗口选择、位移频域特征提取、检测流程完善3个步骤,完成新型检测方法的搭建,实现高负荷下船舶柴油机滑动轴承位移检测方法研究。对比实验结果表明,与现有检测方法相比,应用新型船舶柴油机滑动轴承位移检测方法后,定位精准性、检测灵活性等指标均得到一定程度的提升。
        The existing methods for measuring the displacement of marine diesel engine journal bearings have some disadvantages, such as low positioning accuracy, low detection flexibility and so on. In order to solve the above problems, a new displacement detection method for sliding bearings of marine diesel engine under high load is put forward. Through two steps of standardized autocorrelation coefficient determination and displacement feature clustering, the preparation of high load diesel engine sliding bearing displacement detection is completed. On this basis, through the sliding detection time window selection, displacement frequency domain feature extraction, detection process improvement three steps, complete the construction of the new detection method, realize the high load marine diesel engine sliding bearing displacement detection method research. The experimental results show that, compared with the existing detection methods, the positioning accuracy, detection flexibility and other indicators of the new marine diesel engine journal bearing displacement detection methods have been improved to a certain extent.
引文
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    [3]张云强,张培林,王怀光,等.基于变分模式分解的滑动轴承摩擦故障特征提取与状态识别[J].内燃机工程,2017,38(4):89-96.
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