龚廷恺. 基于改进l1趋势滤波的滚动轴承故障诊断[J]. 南昌航空大学学报(自然科学版), 2017, 31(4): 86-90. DOI: 10.3969/j.issn.1001-4926.2017.04.014
引用本文: 龚廷恺. 基于改进l1趋势滤波的滚动轴承故障诊断[J]. 南昌航空大学学报(自然科学版), 2017, 31(4): 86-90. DOI: 10.3969/j.issn.1001-4926.2017.04.014
GONG Ting-kai. Rolling Element Bearing Fault Diagnosis Based on Improved l1 Trend Filtering[J]. Journal of nanchang hangkong university(Natural science edition), 2017, 31(4): 86-90. DOI: 10.3969/j.issn.1001-4926.2017.04.014
Citation: GONG Ting-kai. Rolling Element Bearing Fault Diagnosis Based on Improved l1 Trend Filtering[J]. Journal of nanchang hangkong university(Natural science edition), 2017, 31(4): 86-90. DOI: 10.3969/j.issn.1001-4926.2017.04.014

基于改进l1趋势滤波的滚动轴承故障诊断

Rolling Element Bearing Fault Diagnosis Based on Improved l1 Trend Filtering

  • 摘要: 针对轴承故障的振动信号滤波问题,提出了改进l1趋势滤波方法。该方法滤波效果由规则化参数决定,一般根据原始信号的特征信息来确定这个参数。为了提升适用性,最佳规则化参数通过与最大值之间的线性关系来选取。通过实际轴承的内、外圈故障振动信号分析发现,该方法能提取轴承故障特征。同时,相比于经验模态分解方法,改进方法具有更好的特征提取效果。

     

    Abstract: In order to remove the noise in the vibration signals of fault bearings, improved l1 trend filtering is exploited. In this method, regularization parameter is used to control the performance of the filtering method, and is experimentally determined through the feature information of raw signals. It is inconvenient to real applications. In this case, the suited parameter is selected based on the linear relation between it and its maximum. By analyzing the two vibrations measured from the bearing with an outer race fault and an inner race fault respectively, the results demonstrate that the proposed method is effective and robust to diagnose the two bearing faults. At the same time, empirical mode decomposition is adopted for further comparisons. It shows that the improved approach has better performance in the fault feature extractions.

     

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