In order to improve the accuracy of escalator sprocket bearing fault diagnosis,the problem of the feature extraction method of bearing vibration signal is addressed.In this paper,empirical mode is used to decompose th...In order to improve the accuracy of escalator sprocket bearing fault diagnosis,the problem of the feature extraction method of bearing vibration signal is addressed.In this paper,empirical mode is used to decompose the original signal,and the optimal modal component among the multiple modal components is obtained after the optimization decomposition is selected by the envelope spectrum method,and the multi-angle feature measure is introduced to extract the fault characteristic value.According to the vibration characteristics of the bearing vibration signal data,a bearing signal feature group that is more inclined to the fault feature category information is established,which avoids the absolute problem of extracting a single metric feature.The fuzzy C-means clustering algorithm is used to cluster the sample data with similar characteristics into the same cluster area,which effectively solves the problem that a single measurement analysis cannot characterize the complex internal characteristics ofthe bearing vibration signal.展开更多
针对光伏系统直流侧故障电弧检测问题,提出了一种基于集合经验模态分解(ensemble empirical mode decomposition,EEMD)和模糊C均值聚类(fuzzy C means clustering,FCM)的组合故障检测方法。首先采用EEMD分解法将光伏系统直流母线电...针对光伏系统直流侧故障电弧检测问题,提出了一种基于集合经验模态分解(ensemble empirical mode decomposition,EEMD)和模糊C均值聚类(fuzzy C means clustering,FCM)的组合故障检测方法。首先采用EEMD分解法将光伏系统直流母线电流信号分解为若干个本征模态分量(intrinsic mode function,IMF),再利用模糊熵算法将本征模态分量熵值化,并从中提取能够表征故障电弧的特征向量,然后通过FCM算法进行故障电弧识别。理论分析和实验结果验证了所提方法的可行性和正确性。最后考虑到光伏系统的复杂性,研究了不同工况以及外界因素对故障电弧检测的影响,并通过仿真和实验数据证明所提检测方法具有良好的抗干扰能力。展开更多
文摘In order to improve the accuracy of escalator sprocket bearing fault diagnosis,the problem of the feature extraction method of bearing vibration signal is addressed.In this paper,empirical mode is used to decompose the original signal,and the optimal modal component among the multiple modal components is obtained after the optimization decomposition is selected by the envelope spectrum method,and the multi-angle feature measure is introduced to extract the fault characteristic value.According to the vibration characteristics of the bearing vibration signal data,a bearing signal feature group that is more inclined to the fault feature category information is established,which avoids the absolute problem of extracting a single metric feature.The fuzzy C-means clustering algorithm is used to cluster the sample data with similar characteristics into the same cluster area,which effectively solves the problem that a single measurement analysis cannot characterize the complex internal characteristics ofthe bearing vibration signal.
文摘针对光伏系统直流侧故障电弧检测问题,提出了一种基于集合经验模态分解(ensemble empirical mode decomposition,EEMD)和模糊C均值聚类(fuzzy C means clustering,FCM)的组合故障检测方法。首先采用EEMD分解法将光伏系统直流母线电流信号分解为若干个本征模态分量(intrinsic mode function,IMF),再利用模糊熵算法将本征模态分量熵值化,并从中提取能够表征故障电弧的特征向量,然后通过FCM算法进行故障电弧识别。理论分析和实验结果验证了所提方法的可行性和正确性。最后考虑到光伏系统的复杂性,研究了不同工况以及外界因素对故障电弧检测的影响,并通过仿真和实验数据证明所提检测方法具有良好的抗干扰能力。