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Rolling bearing performance degradation condition recognition based on mathematical morphological fractal dimension and fuzzy C-means

机译:基于数学形态学分形尺寸和模糊C型滚动轴承性能降解条件识别

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摘要

In allusion to performance degradation condition recognition issue for rolling bearing, a method based on mathematical morphological fractal dimension (MMFD as abbreviation) and fuzzy C-means algorithm (FCM as abbreviation) is proposed in this paper. MMFD of vibrating signal is able to describe the complexity and irregularity from the perspective of fractal, its effectiveness and stability is justified by means of signal simulation. On this basis, considering fuzzy character of performance degradation condition boundary, FCM is introduced in degradation condition recognition. Rolling bearing fatigue life enhancement testing was carried out in Hangzhou Bearing Test & Research Center, the whole life data was gathered and applied in this paper, the result shows that the proposed technique of MMFD-FCM has an excellent effect. (C) 2017 Elsevier Ltd. All rights reserved.
机译:在本文提出了一种基于数学形态学分形尺寸(MMFD作为缩写)和模糊C-MEAC算法(FCM作为缩写)的基于数学形态分形维数(MMFD)的方法。 振动信号的MMFD能够从分形的角度描述复杂性和不规则性,其有效性和稳定性通过信号模拟是合理的。 在此基础上,考虑到性能下降条件边界的模糊特性,在降解条件识别中引入了FCM。 滚动轴承疲劳寿命增强试验在杭州轴承测试和研究中心进行,整个寿命数据都收集和应用,结果表明,所提出的MMFD-FCM技术具有出色的效果。 (c)2017 Elsevier Ltd.保留所有权利。

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