基于数学形态梯度谱熵的性能退化特征提取方法及其应用

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振动与冲击 ›› 2016, Vol. 35 ›› Issue (16) : 86-90.

论文

刘鹏，李洪儒，许葆华

作者信息 +

A performance degradation feature extraction method and its application based on mathematical morphological gradient spectrum entropy

Liu Peng, Li Hong-ru, Xu Bao-hua

Author information +

文章历史 +

摘要

针对数学形态谱熵难以准确描述信号的形态复杂度以及性能退化趋势评价效果不理想的问题，提出一种基于数学形态梯度谱熵的性能退化特征提取方法。该方法利用形态梯度算子在信号处理中能有效提取故障特征信息并抑制噪声的优势，将其引入到形态谱熵的定义中，得到数学形态梯度谱熵的概念。通过对仿真信号进行分析，验证了所提出的形态梯度谱熵作为信号复杂度指标的合理性与有效性。最后，将该方法应用到滚动轴承的性能退化研究中，结果表明，形态梯度谱熵能有效反映滚动轴承的性能退化趋势。

Abstract

In view of that morphology spectrum entropy fails to accurately describe the morphological complexity of signals and its evaluation effect of performance degradation trend is not ideal, a performance degradation feature extraction method based on mathematical morphological gradient spectrum entropy was proposed. In the paper, the method introduced morphological gradient algorithm which could effectively extract fault feature information and remove interference components in the signal processing into the definition of morphology spectrum entropy, and got the conception of mathematical morphological gradient spectrum entropy. The simulation signal verified the rationality and effectiveness of morphological gradient spectrum entropy as a signal complexity index. Lastly the rolling bearing performance degradation study result proved that, morphological gradient spectrum entropy could reflect rolling bearing's performance degradation trend.

导出引用

刘鹏，李洪儒，许葆华. 基于数学形态梯度谱熵的性能退化特征提取方法及其应用[J]. 振动与冲击, 2016, 35(16): 86-90

Liu Peng, Li Hong-ru, Xu Bao-hua. A performance degradation feature extraction method and its application based on mathematical morphological gradient spectrum entropy[J]. Journal of Vibration and Shock, 2016, 35(16): 86-90

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