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自适应冗余提升小波降噪分析及轴承故障识别应用 被引量:9

Adaptive redundant lifting wavelet denoising analysis and its application in bearing fault identification
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摘要 为有效识别机械设备中滚动轴承微弱故障信息,提出自适应冗余提升小波降噪方法。据待分解低频尺度系数所含不同特征,应用范数准则自适应选取最匹配该尺度系数特征的小波函数。引入多孔算法,用以通过冗余性保证逐层分解后各尺度系数与小波系数所含丰富信息量。对各层小波系数采用变尺度阈值降噪算法,并对降噪后系数进行重构及包络谱分析,提取滚动轴承故障特征。通过对实验台轴承混合故障信号与现场实际信号分析表明,故障识别较好,从而验证该方法的有效性。 Here, an adaptive redundant lifting wavelet denoising method was proposed to realize effective identification of weak fault information of roller bearings in mechanical equipments. According to the different features contained in scale coefficients to be decomposed, wavelets matching optimally these features were adaptively selected based on the norm criteria. Meanwhile, a mulit-hole algorithm was introduced here to guarantee sufficient information in each scale coefficient and wavelet coefficient after every decomposition. Then, the variable scale threshold denoising algorithm was applied to process each wavelet coefficient, the reconstruction and envelope spectral analysis of these coefficients were performed to extract fault features of roller bearings. Analysis results with the method presented above for compound fault signals of a bearing test table and field practical signals showed that bearing fault identification can be well realized and the proposed method is effective.
出处 《振动与冲击》 EI CSCD 北大核心 2013年第7期54-57,共4页 Journal of Vibration and Shock
基金 国家高新技术研究发展863计划(2009AA04Z417) 北京市自然科学基金项目(3112004)
关键词 自适应 冗余提升小波 降噪 轴承 故障识别 adaptive redundant lifting wavelet denoising bearing fault identification
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