Fault component extraction of engine based on abnormal sound

NING Da-yong; GONG Yong-jun; SUN Chang-le; HOU Jiao-yi; ZHANG Zeng-meng

doi:10.3788/OPE.20152313.0313

您当前的位置：

首页 >

文章列表页 >

Fault component extraction of engine based on abnormal sound

更新时间：2020-08-13

- Fault component extraction of engine based on abnormal sound
- Optics and Precision Engineering Vol. 23, Issue 10z, Pages: 313-319(2015)
- 作者机构：
  
  大连海事大学交通运输装备与海洋工程学院,辽宁大连,116026
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20152313.0313
  CLC： TM31
- Received：15 May 2015，
  
  Revised：10 July 2015，
  
  Published：14 November 2015
- 稿件说明：
移动端阅览
宁大勇, 弓永军, 孙长乐等. 基于异响信号的发动机故障成分提取[J]. 光学精密工程, 2015,23(10z): 313-319

NING Da-yong, GONG Yong-jun, SUN Chang-le etc. Fault component extraction of engine based on abnormal sound[J]. Editorial Office of Optics and Precision Engineering, 2015,23(10z): 313-319
宁大勇, 弓永军, 孙长乐等. 基于异响信号的发动机故障成分提取[J]. 光学精密工程, 2015,23(10z): 313-319 DOI： 10.3788/OPE.20152313.0313.

NING Da-yong, GONG Yong-jun, SUN Chang-le etc. Fault component extraction of engine based on abnormal sound[J]. Editorial Office of Optics and Precision Engineering, 2015,23(10z): 313-319 DOI： 10.3788/OPE.20152313.0313.

摘要

提出了基于异响信号的故障成分提取方法

以实现对发动机异响故障的在线诊断。该方法分别利用错位叠加法、互相关函数、叠加段起始位置搜寻方法来提取异响信号成份。首先

根据异响信号局部极值出现的位置确定异响信号产生的起始位置

并确定各截取信号段的长度;将各截取的信号段进行线性相加

实现故障成分的提取。然后

对信号的误差进行分析

提出误差的影响因素。最后

利用互相关性分析判断截取信号的准确性。实验结果表明:通过错位叠加算法提取的多段异响信号故障成分相关性达到75%以上

可以用于故障诊断。根据错位叠加算法提取异响信号故障成分无需进行时频转换

方法简单易行

提取的故障成分的波形准确性高。

Abstract

An approach of fault component extraction for abnormal sound was proposed to realize the online automatic diagnosis of engines. In the method

an overlap superimposed method was proposed for noise elimination

and the correlation analysis and the beginning position of dislocation superimposed interception signal searching method were used to establish the signal analysis system. Firstly

the local extreme value of abnormal sound was used to determine the beginning position of abnormal sound appearance and to calculate the length of interception signal according to the separation distance of the adjacent starting points. Then

these intercepted signals were linearly superposed together to acquire the fault component. Furthermore

the error of extracted signal was analyzed and the effect factors on the errors were obtained. Finally

the cross-correlation analysis method was adopted to identify the results. Experimental results indicate that the relevance among several extracted signals reaches to 75% by the dislocation method

which can be used for fault diagnosis. The method is no complicated time frequency conversion process in extracting fault components and has advantages in simple operation and high accuracy.

关键词

Keywords

references

蔡振雄, 李寒林, 林金表,等. 船舶柴油机拉缸故障振动诊断技术[J]. 上海海事大学学报, 2007, 28(1):84-88. CAI ZH X, LI H L, LIN J B,et al.. Marine diesel cylinder score fault diagnosis using vibration technology[J]. Journal of Shanghai Maritime University, 2007, 28(1):84-88.(in Chinese)

贾继德,葛同民,杨万成,等. 基于非平稳周期循环特征增强的柴油机失火故障诊断研究[J]. 内燃机工程,2013,34(1):67-70. JIA J D, GE T M, YANG W CH,et al.. Research on diagnosis of diesel engine misfire fault bsed on non-stationary cycle feature enhancement[J]. Chinese Internal Combustion Egnine Engineering, 2013, 34(1):67-70.(in Chinese)

马文亮,吴汉涛,李慧军,等. 冬季发动机拉缸问题研究[J]. 内燃机, 2012,4(8):54-56. MA W L, WU H T, LI H J,et al.. Research of scratching fault from engine in winter[J]. Internal Combustion Engines, 2012,4(8):54-59.(in Chinese)

苏欣,王奇峰. 某四缸柴油机冲缸垫故障分析[J]. 内燃机, 2014(2):5-7. SU X, WANG Q F. Trouble-shooting for Wrecked cylinder head Gasket in Certain Four-cylinder Diesel Engine[J]. Internal Combustion Engines, 2014, 2(1):5-7.(in Chinese)

高红霞,吴丽璇,徐寒,等. 微焦点X射线图像乘性加性混合噪声的去除[J]. 光学精密工程, 2014,22(11):3100-3113. GAO H X, WU L X, XU H,et al..Denoising method of micro-focus X-ray images corrupted with mixed multiplicative and additive noise[J]. Optics and Precision Engineering, 2014, 22(11):3100-3113.(in Chinese)

邢志娜,瞿军,王菊香. 重载车辆发动机油液的多技术监测方法[J]. 兵工自动化,2012,31(5):83-86. XING ZH N, QU J, WANG J X. Oil multi-technique monitoring methods of heavy-duty vehicle engine[J]. Ordnace Industry Automation, 2012, 31(5):83-86.(in Chinese)

张梅军,陈江海,侯宝科. 发动机故障分形诊断中噪声信号的分析和处理[J]. 解放军理工大学学报(自然科学版), 2006,7(4):380-384. ZHANG M J, CHEN J H, HOU B K. Analysis and processing of noise in engine fault diagnosis by fractal method[J]. Journal of PLA University of Science and Technology, 2006, 7(4):380-384.(in Chinese)

杨诚,黄金旺,杨振冬,等. 摩托车发动机异响在线检测系统开发[J]. 汽车工程学报, 2013, 3(5):374-379. YANG CH, HUANG J W, YANG ZH D,et al.. Development of online sound abnormality detection system of motorcycle engines[J]. Chinese Journal of Automotive Engineering, 2013,3(5):374-379.(in Chinese)

赵立荣,朱玮,曹永刚,等. 改进的加速鲁棒特征算法在特征匹配中的应用[J]. 光学精密工程, 2013, 21(12):3263-3271. ZHAO L R, ZHU W, CAO Y G,et al.. Application of improved SURF algorithm to feature matching[J]. Opt. Precision Eng., 2013, 21(12):3263-3271.(in Chinese)

严新平,李志雄,张月雷,等. 船舶柴油机摩擦磨损监测与故障诊断关键技术研究进展[J]. 中国机械工程,2013,24(10):1413-1419. YAN X P, LI ZH X, ZHANG Y L,et al.. Study on key techniques of wear monitoring and fault diagnosis for marine diesel engines a review[J]. China Mechanical Engineering, 2013, 24(10):1413-1419.(in Chinese)

钱作勤,周祥军. 内燃机拉缸故障诊断型专家系统[J]. 武汉理工大学学报(交通科学与工程版), 2004,28(5):634-636. Qian Z Q, ZHOU X J. A brief diagnosis expert system for cylinder scoring malfunction of internal combustion engine[J]. Journal of Wuhan University of Technology(Transportation Science & Engineering), 2004,28(5):634-636.(in Chinese)

崔园园,王伯雄,柳建楠,等. 数字超声波信号中有色噪声的自适应滤波[J]. 光学精密工程, 2014, 22(12):3377-3383. CUI Y Y, WANG B X, LIU J N,et al.. Adaptive filtering for colored noise in digital ultrasonic signals[J]. Opt. Precision Eng., 2014, 22(12):3377-3383.(in Chinese)

Views

691

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰