CAJ | 학술논문

为解决在强背景噪声条件下滚动轴承故障诊断问题,开展基于能量特征和小波降噪的总体经验模态分解(EEMD)研究。首先以仿真信号为研究对象,对其进行总体经验模态分解,得到9个固有模态函数(IMF)和1个余项( Res),然后考虑各模态函数的能量特征,将分解后的9个IMF分量与原始信号的能量比作为判断标准,剔除附加5个低频分量,最终得到4个有效的IMF分量和1个余项,与仿真信号相符。在仿真信号分析的基础上,对含噪声信号的滚动轴承故障信号进行故障诊断试验研究,采集信号经小波降噪后,利用总体平均经验模态分解并结合能量特征,得到3个IMF分量和1个余项,然后对3个IMF分量进行包络谱分析,提取故障特征频率157.5 Hz,与滚动轴承故障内圈特征频率157.9 Hz相比,误差为0.25%,说明该方法能很好地提取含有噪声信号的轴承故障信息。该研究为强背景噪声下滚动轴承故障信息的提取提供了一种有效的方法。
위해결재강배경조성조건하곤동축승고장진단문제,개전기우능량특정화소파강조적총체경험모태분해(EEMD)연구。수선이방진신호위연구대상,대기진행총체경험모태분해,득도9개고유모태함수(IMF)화1개여항( Res),연후고필각모태함수적능량특정,장분해후적9개IMF분량여원시신호적능량비작위판단표준,척제부가5개저빈분량,최종득도4개유효적IMF분량화1개여항,여방진신호상부。재방진신호분석적기출상,대함조성신호적곤동축승고장신호진행고장진단시험연구,채집신호경소파강조후,이용총체평균경험모태분해병결합능량특정,득도3개IMF분량화1개여항,연후대3개IMF분량진행포락보분석,제취고장특정빈솔157.5 Hz,여곤동축승고장내권특정빈솔157.9 Hz상비,오차위0.25%,설명해방법능흔호지제취함유조성신호적축승고장신식。해연구위강배경조성하곤동축승고장신식적제취제공료일충유효적방법。
To solve the problem of fault diagnosis of rolling bearing under heavy background noise, an ensemble empirical mode decomposition ( EEMD ) method combined with energy feature and wavelet de-noising was studied. Taking a simulated signal as the research object, ensemble empiri-cal mode decomposition was made to obtain nine intrinsic mode functions ( IMFs) and a residue. Then the energy ratio of the nine IMF components after decomposition and the original signal was used as the standard of judgment by considering energy features of the IMFs. Finally four effective IMF components and a residue were obtained by eliminating the other five redundant low-frequency components, which is consistent with the simulated signal. According to this analysis strategy of sim-ulation signal, a test of fault diagnosis of rolling bearing was taken. After wavelet de-noising the col-lected signals and utilizing the EEMD method combed with energy feature, three IMF components and a residue were obtained. Then, envelope spectrum analysis of the three IMF components was done to extract the fault feature frequency of 157. 5 Hz. Compared with inner race characteristic fre-quency of 157. 9 Hz, the calculating error is 0. 25%, it indicatesthat bearing fault feature can be well extracted from the collected signal with heavy noise. The study proposes an effective method to ex-tract fault features of rolling bearing under the background of heavy noise.