基于滑移信息熵与最优滤波器构建的故障诊断方法

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振动与冲击 ›› 2017, Vol. 36 ›› Issue (21) : 34-39.

论文

童水光1，徐剑2，从飞云1，唐宁2，张依东1

作者信息 +

Fault diagnosis method based on slip information entropy and optimal filter construction

Shuiguang Tong1, Jian Xu2 , Feiyun Cong1, Ning Tang2, Yidong Zhang1

Author information +

文章历史 +

摘要

以故障信号局部包含信息的差异性为基础，结合相空间重构和信息熵理论，提出滑移信息熵序列对故障信息进行局部冲击特征识别。在此基础上，引入最小熵反卷积、最优滤波器构建等理论，成功实现了滚动轴承的微弱故障诊断。仿真数据和实验数据分析论证结果表明，本文提出的故障特征提取技术对于滚动轴承微弱冲击故障特征具有优越的识别和提取能力，对于实现滚动轴承强噪声背景下故障智能诊断具有重要的意义。

Abstract

Based on the difference in local feature of fault signal，the concept of slip information entropy sequence is put forward combined with the phase space reconstruction and information entropy theory to detect the local impulse feature information. The minimum entropy deconvolution, optimal filter construction theory are applied to improve the ability of weak fault diagnosis of rolling bearings. The proposed method is successfully applied into the fault feature extraction of rolling bearings. The experimental data analysis result shows that the proposed method has a good ability at weak shock fault feature extraction. The work of this paper has important implications in fault intelligent diagnosis of rolling bearings under strong noise background.

导出引用

童水光1，徐剑2，从飞云1，唐宁2，张依东1. 基于滑移信息熵与最优滤波器构建的故障诊断方法[J]. 振动与冲击, 2017, 36(21): 34-39

Shuiguang Tong1, Jian Xu2,Feiyun Cong1, Ning Tang2, Yidong Zhang1. Fault diagnosis method based on slip information entropy and optimal filter construction[J]. Journal of Vibration and Shock, 2017, 36(21): 34-39

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