| 引用本文: | 万书亭,张雄,庞彬,豆龙江.自适应陷波理论及其在轴承故障诊断中的应用[J].电力自动化设备,2018,(9): |
| WAN Shuting,ZHANG Xiong,PANG Bin,DOU Longjiang.Application of adaptive trap theory in bearing fault diagnosis[J].Electric Power Automation Equipment,2018,(9): |
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| 摘要: |
| 滚动轴承故障冲击特征易被工频载波信号淹没，而传统的信号降噪方法对工频干扰不具有针对性，所以将工频陷波理论引入到轴承故障诊断中。由于陷波的窄带滤波特性，其对中心频率及带宽参数变化较为敏感，通过粒子群多参数寻优，以时域峭度最大原则对陷波器中心频率及带宽进行自适应选取，以时域波形匹配方差作为评价指标验证陷波对故障冲击特性的还原能力。试验分析表明自适应陷波可以有效地从工频调制信号中解调出故障冲击特征，对陷波后信号进行包络谱分析，其故障特征谱线得到增强，辅助以集合经验模态分解(EEMD)、变分模态分解(VMD)去噪方法，可以得到更理想的效果。 |
| 关键词: 滚动轴承 故障诊断 粒子群优化算法 自适应陷波器 |
| DOI：10.16081/j.issn.1006-6047.2018.09.021 |
| 分类号:TH212;TH213.3 |
| 基金项目: |
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| Application of adaptive trap theory in bearing fault diagnosis |
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WAN Shuting, ZHANG Xiong, PANG Bin, DOU Longjiang
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Department of Mechanical Engineering, North China Electric Power University, Baoding 071003, China
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| Abstract: |
| Since the characteristics of the bearing fault shock are easily submerged by the frequency carrier signal, and the traditional signal noise reduction methods are not specific to the power frequency interference, so the wave trap theory is introduced to the bearing fault diagnosis. Owing to the narrow band filter characteristics, the trap wave is sensitive to the change of center frequency and bandwidth parameters. Through the particle swarm multiple para-meter optimization, the time-domain kurtosis maximum principle is used to adaptively select the center frequency and bandwidth of trap filter, and the time-domain waveform matching variance is taken as the evaluation index to verify that the trap can restore the fault shock characteristic. Experimental analysis shows that the fault shock characteristic can be effectively demodulated from the power frequency modulation signal. The envelope spectrum analysis of signal after the trap is carried out, which shows that the fault characteristic spectral lines are enhanced, and more ideal results will be obtained with the help of the traditional EEMD(Ensemble Empirical Mode Decomposition) and VMD(Variational Mode Decomposition) denoising method. |
| Key words: rolling bearing fault diagnosis particle swarm optimization algorithm adaptive trap filter |
