[1]马宏陆,葛琳琳,牛 强,等.一种基于改进EMD的风机振动信号异常检测方法[J].南京师范大学学报(自然科学版),2017,40(01):55.[doi:10.3969/j.issn.1001-4616.2017.01.009]
　Ma Honglu,Ge Linlin,Niu Qiang,et al.An Improved Approach for Vibration Signal AnomalyDetection of Ventilator Based on EMD[J].Journal of Nanjing Normal University(Natural Science Edition),2017,40(01):55.[doi:10.3969/j.issn.1001-4616.2017.01.009]
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一种基于改进EMD的风机振动信号异常检测方法()
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《南京师范大学学报》（自然科学版）[ISSN:1001-4616/CN:32-1239/N]

卷:
第40卷
期数:
2017年01期
页码:
55
栏目:
·数学与计算机科学·
出版日期:
2017-03-31

文章信息/Info

Title:
An Improved Approach for Vibration Signal AnomalyDetection of Ventilator Based on EMD
文章编号:
1001-4616(2017)01-0055-10
作者:
马宏陆; 葛琳琳; 牛 强; 夏士雄
中国矿业大学计算机科学与技术学院,江苏 徐州 221116
Author(s):
Ma Honglu; Ge Linlin; Niu Qiang; Xia Shixiong
College of Computer Science and Technology,China University of Mining and Technology,Xuzhou 221116,China
关键词:
EMD; 风机异常检测; 过冲问题; 端点效应; 端点飞翼
Keywords:
empirical mode decomposition(EMD); ventilator anomaly detection; overshoot problem; end effect; end swing
分类号:
TP181
DOI:
10.3969/j.issn.1001-4616.2017.01.009
文献标志码:
A
摘要:
针对基于Empirical Mode Decomposition(EMD)的风机振动信号异常检测中噪声污染、CSI拟合包络线导致的过冲问题,端点效应引发的端点飞翼现象三点问题,提出了一种改进的EMD算法. 该算法首先引进小波方法对原始数据进行降噪处理,再用边界特征尺度匹配方法对原始信号两端进行端点延拓处理,降低端点效应,同时结合3次Hermite插值拟合法的良好柔性来拟合包络线,以获得均值曲线. 实验表明,利用该改进的EMD方法得到矿井风机振动边际谱,能清晰地得出风机振动信号特性,消除了过冲的影响,对端点效应也有了明显改善,提高了风机异常检测的准确率.
Abstract:
Focused on the issues that caused by the noise pollution of ventilator,the over shoot problem caused by CSI fitting Envelop curve,and the end swing problems caused by the end effect,an Improve-Empirical Mode Decomposition algorithm was proposed. Firstly,the algorithm introduces the wavelet method to denoise the original signal data,then uses matching boundary feature extension method to effectively restrain the end effect while combining with the good flexibility of Cubic Hermite to fit the envelope to obtain the average curve. Experiments shows,by analyzing the ventilator vibration marginal spectrum with the improved algorithm,ventilator vibration signal characteristics can be shown clearly,the improved algorithm can eliminate the overshoot problems,markedly improve the endpoint effect and improve the accuracy of the ventilator anomaly detection.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2016-05-15.
基金项目:江苏省产学研联合创新资金前瞻性联合研究项目(BY2014028-09).
通讯联系人:马宏陆,硕士,研究方向:传感器定位、人工智能. E-mail:cumtmahonglu@163.com
更新日期/Last Update: 1900-01-01