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基于能量聚集性的轴承复合故障诊断
引用本文:陈彦龙,张培林,李兵,徐超.基于能量聚集性的轴承复合故障诊断[J].噪声与振动控制,2013,33(1):191-196.
作者姓名:陈彦龙  张培林  李兵  徐超
作者单位:( 军械工程学院 一系,石家庄 050003 )
基金项目:国家自然科学基金项目(50705097);清华大学国家重点实验室开放基金资助项目(SKLTKF09B06)
摘    要:轴承复合故障类型多样,且部分故障的特征频率相近噪声污染严重。采用经验模态分解(EMD)的方法,在强噪声背景下会引起相近频率故障成分的无法识别,同时也难以提取微弱的故障信号。由此,提出一种基于能量聚集性的轴承复合故障诊断方法。首先借助离散余弦变换(DCT)的频域能量聚集性和奇异值分解(SVD)的时域能量聚集性,对轴承复合故障信号进行预处理,实现降噪并分离频率相近的微弱故障信号。然后对分离出来的不同故障信号进行经验模态分解,去除伪分量,对剩余的本征模态函数进行频谱分析。最后,根据本征模态函数的频谱诊断故障。仿真信号和实测轴承故障诊断信号分析表明,与直接使用EMD进行轴承复合故障诊断相比,该方法能够在强背景噪声下准确分离频率相近的微弱故障分量,改善复合故障诊断的准确性。

关 键 词:振动与波  离散余弦变换  奇异值分解  经验模态分解  复合故障诊断  微弱信号  
收稿时间:2012-02-29

Combined Bearing Fault Diagnosis Method Based on Energy Aggregation
CHEN Yan-long,ZHANG Pei-lin,LI Bing,XU Chao.Combined Bearing Fault Diagnosis Method Based on Energy Aggregation[J].Noise and Vibration Control,2013,33(1):191-196.
Authors:CHEN Yan-long  ZHANG Pei-lin  LI Bing  XU Chao
Affiliation:(First Department,Ordnance Engineering College,Shijiazhuang 050003,China)
Abstract:Combined bearing fault types are varied, and fault signals are interferenced with strong noise. Characteristic frequencies of several faults are adjacent. Aiming at disadvantadges of EMD that it is hard to resolute near frequency fault signals and difficult to separate weak fault signals under strong noise background, a bearing combined fault diagnosis scheme based on energy aggregation is proposed. Firstly, energy aggregation in frequency domain of discrete cosine transform(DCT) and energy aggregation in time domain of singular value decomposition(SVD) are used to preprocess combined fault signals, in order to denoise and separate weak fault signals which frequencies are close. Secondly, separeted signals are analyzed by empirical mode decomposition(EMD), false intrinsic mode functions(IMFs) found by energy analysis are deleted and frequency spectra of right IMFs are analyzed. At last, combined faults are diagnosed by IMFs spectra. The simulated signal and the actual bearing fault signal analysis results show that compared with EMD, this method can resolute adjacent frequency fault signals and distill weak signals under strong noise, and detect combined faults correctly.
Keywords:
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