重庆大学学报2024，Vol.47Issue(3)：96-106,11.DOI:10.11835/j.issn.1000-582X.2022.011
增强组合差分乘积形态学滤波的轴承故障特征提取方法
Bearing fault feature extraction method based on enhanced combination difference multiply morphological filter
摘要
Abstract
To address the limitations of conventional time-frequency domain feature extraction methods when dealing with the non-linear, non-stationary and strongly noisy characteristics of rolling bearing fault signals, a bearing fault feature extraction method based on an enhanced combination difference multiply morphological filter is proposed in this study. Based on the understanding of the positive and negative shock pulse extraction characteristics of the four basic operations of mathematical morphology, a new combination difference multiply operator (CDMO) is constructed. This CDMO has the ability to simultaneously extract positive and negative shock pulses by combining cascade, difference and multiply operations. The gradient multiply operation that is more sensitive to pulse extraction is utilized to achieve comprehensive fault information extraction. The fault characteristic frequency ratio index is introduced to optimize the parameters of the CDMO structural elements. This optimization modifies the geometric characteristics of the signal to be processed, allowing for the extraction of signal characteristic information that matches the structural elements. Following CDMO filtering, third-order cumulant slice spectrum technology is employed to suppress Gaussian noise and highlight the advantages of secondary coupling components. This enhances the ability to accurately extract fault feature frequencies and their multiplications, thus improving bearing fault feature extraction and suppressing noise interference. The proposed method's effectiveness is verified by relying on actual engineering signals from two different sources and comparing its performance with classic fault feature extraction methods.关键词
滚动轴承/形态学滤波/三阶累积量切片谱/特征提取Key words
rolling bearing/morphological filtering/third-order cumulant slice spectrum/feature extraction分类
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徐先峰,赵卫峰,邹浩泉,宋亚囡..增强组合差分乘积形态学滤波的轴承故障特征提取方法[J].重庆大学学报,2024,47(3):96-106,11.基金项目
陕西省自然科学基础研究计划资助项目(2019JQ-678) （2019JQ-678）
陕西省重点研发计划资助项目(2021GY-098) （2021GY-098）
西安市智慧高速公路信息融合与控制重点实验室资助项目(ZD13CG46) （ZD13CG46）
长安大学中央高校基本科研业务费专项资金资助项目(300102321504,300102321501,300102321503).Supported by Natural Science Basic Research Program of Shaanxi(2019JQ-678),Key Research and Development Program of Shaanxi Province(2021GY-098),Xi'an Key Laboratory on Intelligent Highway Information Fusion and Controlling(ZD13CG46),and Fundamental Research Funds for the Central Universities,CHD(300102321504,300102321501,300102321503). （300102321504,300102321501,300102321503）
