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国外文献 > 1.轴承 > 2. 轴承类型与部件 > 滚动轴承

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标 题： A comparative study for the selection of an entropy technique to classify bearing faults
用于选择熵技术进行轴承故障分类的比较研究
作 者： Amrinder Singh Minhas
Nipun Sharma
P. K. Kankar
Sukhjeet Singh
关键词： Ball bearings  [滚珠轴承]
Ensemble empirical mode decomposition  [ 经验模态分解]
Approximate entropy  [ 近似熵]
Sample entropy  [ 样本熵]
Multi-Scale entropy  [ 多尺度熵]
出版年： 2019
会议名： 3rd IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT 2019)
第三届IEEE国际电气、计算机与通信技术会议（ICECCT 2019）
届 次： 3rd
摘 要： The diagnosis of faults in ball bearings has been a major challenge especially in the non-stationary environment. This paper presents a comparative study to select a method among the approximate entropy, Sample entropy and Multi-Scale entropy that can successfully be employed as a feature vector for fault classification of ball bearings. The acquired vibration signals from the normal and faulty condition of ball bearing are firstly de-noised and then disintegrated by Ensemble empirical mode decomposition method to various intrinsic mode decompositions. From selected intrinsic mode functions the signals are reconstructed and entropies are computed by the proposed methods. The results demonstrate the effectiveness of the Multi-scale entropy technique been able to effectively differentiate the condition of bearing and can be considered to act as an important fault feature vector.

球轴承故障的诊断一直是一个重大挑战，尤其是在非稳态环境中。本文通过比较研究，选取近似熵、样本熵和多尺度熵等方法，探讨其作为球轴承故障分类特征向量的有效性。首先对轴承正常和故障状态下的振动信号进行去噪处理，然后采用集合经验模分解法将其分解为多个固有模。利用选定的固有模函数重构信号，并采用所提出的方法计算熵值。结果表明，多尺度熵技术能够有效区分轴承状态，可被视为重要的故障特征向量。
中文内容为网络AI翻译，专业术语未必准确，仅供参考。

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