机械故障特征信息提取的ICA信息融合方法

毋文峰; 宋建社; 陈小虎; 江克侠; 李浩

doi:10.13433/j.cnki.1003-8728.2016.0719

机械故障特征信息提取的ICA信息融合方法

doi: 10.13433/j.cnki.1003-8728.2016.0719

1.
武警警官学院, 成都 610213;
2.
第二炮兵工程大学, 西安 710025;
3.
武警工程大学, 西安 710086

基金项目:

国家自然科学基金项目（61132008）资助

详细信息

作者简介:
毋文峰(1978-),讲师,博士,研究方向为系统工程,智能信号处理,peakxde@163.com

通讯作者:
宋建社(联系人),教授,博士,gfzd_mail@163.com

计量
- 文章访问数: 205
- HTML全文浏览量: 45
- PDF下载量: 6
- 被引次数: 0
出版历程
- 收稿日期: 2014-05-30
- 刊出日期: 2016-07-05

ICA Fusion for Feature Extraction of Mechanical Fault

1.
Officers College of the CAPF, Chengdu 610213, China;
2.
The Second Artillery Engineering University, Xi'an 710025, China;
3.
Engineering University of the CAPF, Xi'an 710086, China

摘要

摘要: 峭度和负熵是盲信号独立性的两个自然测度，可以被用来捕捉机械振动信号信息的动态变化特征，并提取机械设备的故障特征信息。峭度和负熵是从两个不同的角度和层面阐释机械设备的故障特征信息，信息量是互补的。若将峭度信息和负熵信息融合，则必然能够更全面、更深刻地来表征机械设备的状态。因此引入信息融合的思想，提出基于ICA信息融合的机械故障特征信息提取方法，综合峭度和负熵信息来提取机械设备的故障特征信息。液压齿轮泵模式识别试验表明，该方法可以应用于机械设备的故障特征信息提取。
- 独立成分分析 /
- 信息融合 /
- 特征提取 /
- 模式识别
Abstract: The kurtosis and negentropy, as two natural measures of independence for blind signals, can be utilized to capture the dynamic information characteristics of mechanical vibration signals. The dynamic information can be extracted as their fault features. The kurtosis and negentropy can explain the feature information of mechanical fault from two different viewpoints that the and their information contents are complementary mutually. If the kurtosis information and negentropy information were fused into one feature vector, it can definitely express the running states of machines more comprehensively and more profoundly. A feature extraction method based on ICA fusion for mechanical fault was proposed, which can fuse the kurtosis information and negentropy information as the final optimum feature information. The pattern recognition experiments of hydraulic gear pump indicate that this method can be applied to feature extraction of mechanical equipment.
- independent component analysis /
- information fusion /
- feature extraction /
- pattern recognition

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参考文献(16)

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