基于混沌振子和EEMD的周期信号检测方法

针对含强噪声周期信号的检测，提出基于混沌振子结合集合经验模式分解降噪的检测新方法；针对相位差对检测结果的影响，提出正反导入的检测方法，该方法能有效克服由相位差造成的漏检现象。对仿真信号和故障轴承振动信号的检测效果表明，混沌振子结合集合经验模式分解降噪的方法能有效检测含在强噪声中周期信号，进一步提高了混沌振子对周期信号的检测能力和对噪声的免疫力。...

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Bibliographic Details
Published in电子技术应用 Vol. 40; no. 4; pp. 133 - 136
Main Author 余发军 周凤星
Format Journal Article
LanguageChinese
Published 武汉科技大学冶金自动化与检测技术教育部工程研究中心,湖北武汉430081%武汉科技大学冶金自动化与检测技术教育部工程研究中心,湖北武汉,430081 2014
中原工学院信息商务学院信息工程系,河南郑州,450001
Subjects
周期信号检测
振动信号
混沌振子
集合经验模式分解
periodic signal detection
vibration signal
周期信号检测
混沌振子
振动信号
chaotic oscillator
集合经验模式分解
ensemble empirical mode decomposition
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ISSN0258-7998

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Summary:针对含强噪声周期信号的检测，提出基于混沌振子结合集合经验模式分解降噪的检测新方法；针对相位差对检测结果的影响，提出正反导入的检测方法，该方法能有效克服由相位差造成的漏检现象。对仿真信号和故障轴承振动信号的检测效果表明，混沌振子结合集合经验模式分解降噪的方法能有效检测含在强噪声中周期信号，进一步提高了混沌振子对周期信号的检测能力和对噪声的免疫力。
Bibliography:chaotic oscillator; periodic signal detection; ensemble empirical mode decomposition;vibration signal
For the detection of periodic signal with strong noise, a new method was proposed based on the chaotic oscillator with ensemble empirical mode decomposition de-noising. In view of the phase difference influence on detection results, a positive and ~legative import method was put forward to solve it, which can effectively overcome the omission phenomenon. The detection re- suits of simulation signal and the fault of bearing vibration signal showed that the method can successfully detect the periodic signal in strong noise and enhances the chaotic oscillator detection ability to periodic signal and immunity to noise further.
Yu Fajun, Zhou Fengxing （1. College of Information and Business, Zhongyuan University of Technology, Zhengzhou 451191, China; 2. Metallurgical Automation and Detection Technology ERC of Education Ministry, Wuhan University of Science and Technology Wuhan 430081, China）
11-2305/TN
ISSN:0258-7998