基于滑移信息熵与最优滤波器构建的故障诊断方法  

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

作　　者：童水光[1] 徐剑[2] 从飞云[1] 唐宁 张依东 

机构地区：[1]浙江大学机械设计与自动化研究所,杭州310027 [2]浙江大学热工与动力系统研究所,杭州310027

出　　处：《振动与冲击》2017年第21期34-39,共6页Journal of Vibration and Shock

基　　金：国家自然科学基金(51305392);浙江省自然科学基金(LZ15E050001);流体传动与控制国家重点实验室青年基金(SKLo FP_QN_1501)

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

关 键 词：信息熵 滑移截取 最优滤波器 特征提取 滚动轴承 

分 类 号：TH165.3[机械工程—机械制造及自动化] TN911[电子电信—通信与信息系统]