Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and ...Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and timely. First, FCM clustering is employed to classify the data into different clusters, which helps to estimate whether there is a fault and how many fault types there are. If fault signals exist, the fault vibration signals are then demodulated and decomposed into different frequency bands by MMEMD in order to be analyzed further. In order to overcome the mode mixing defect of empirical mode decomposition (EMD), a novel method called MMEMD is proposed. It is an improvement to masking empirical mode decomposition (MEMD). By adding multi-masking signals to the signals to be decomposed in different levels, it can restrain low-frequency components from mixing in highfrequency components effectively in the sifting process and then suppress the mode mixing. It has the advantages of easy implementation and strong ability of suppressing modal mixing. The fault type is determined by Hilbert envelope finally. The results of simulation signal decomposition showed the high performance of MMEMD. Experiments of bearing fault diagnosis in wind turbine bearing fault diagnosis proved the validity and high accuracy of the new method.展开更多
应用核函数度量的紧致性和分离性,给出了一种新的聚类有效性指标KKW,由KKW指标得到最优聚类数并用于修正核函数模糊聚类算法(MKFCM),由于经过了修正核函数的映射,使原来没有显现的特征突显出来。用MKFCM对Wine和glass数据集进行聚类,每...应用核函数度量的紧致性和分离性,给出了一种新的聚类有效性指标KKW,由KKW指标得到最优聚类数并用于修正核函数模糊聚类算法(MKFCM),由于经过了修正核函数的映射,使原来没有显现的特征突显出来。用MKFCM对Wine和glass数据集进行聚类,每一类的聚类正确度大于90%;对于缺失数据的Wisconsin Breast Cancer数据,错分率为4.72%。该聚类方法在性能上比经典聚类算法有所改进,具有更快的收敛速度以及较高的准确度。仿真实验的结果证实了修正核聚类方法的可行性和有效性。展开更多
为了实现刀具图像的轮廓跟踪,提取轮廓点,采用Matlab.NET component与Mi-crosoft Visual Studio C Sharp混和编程技术,并运用模糊C均值聚类(Fuzzy C Mean cluste-ring,FCM)算法对图像进行处理,返回分割阈值,实现对灰度图像的分割.同时...为了实现刀具图像的轮廓跟踪,提取轮廓点,采用Matlab.NET component与Mi-crosoft Visual Studio C Sharp混和编程技术,并运用模糊C均值聚类(Fuzzy C Mean cluste-ring,FCM)算法对图像进行处理,返回分割阈值,实现对灰度图像的分割.同时为避免出现轮廓断点,引入编码技术,再经过8邻域搜索目标轮廓,最终完成刀具图像轮廓跟踪.与微分算子的边缘检测对比,本文算法利用FCM分割图像,又引入编码技术,实现轮廓连续的跟踪检测,该方法分割效果较好,界面编写简单,运行简便.展开更多
基金Supported by National Key R&D Projects(Grant No.2018YFB0905500)National Natural Science Foundation of China(Grant No.51875498)+1 种基金Hebei Provincial Natural Science Foundation of China(Grant Nos.E2018203439,E2018203339,F2016203496)Key Scientific Research Projects Plan of Henan Higher Education Institutions(Grant No.19B460001)
文摘Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and timely. First, FCM clustering is employed to classify the data into different clusters, which helps to estimate whether there is a fault and how many fault types there are. If fault signals exist, the fault vibration signals are then demodulated and decomposed into different frequency bands by MMEMD in order to be analyzed further. In order to overcome the mode mixing defect of empirical mode decomposition (EMD), a novel method called MMEMD is proposed. It is an improvement to masking empirical mode decomposition (MEMD). By adding multi-masking signals to the signals to be decomposed in different levels, it can restrain low-frequency components from mixing in highfrequency components effectively in the sifting process and then suppress the mode mixing. It has the advantages of easy implementation and strong ability of suppressing modal mixing. The fault type is determined by Hilbert envelope finally. The results of simulation signal decomposition showed the high performance of MMEMD. Experiments of bearing fault diagnosis in wind turbine bearing fault diagnosis proved the validity and high accuracy of the new method.
文摘应用核函数度量的紧致性和分离性,给出了一种新的聚类有效性指标KKW,由KKW指标得到最优聚类数并用于修正核函数模糊聚类算法(MKFCM),由于经过了修正核函数的映射,使原来没有显现的特征突显出来。用MKFCM对Wine和glass数据集进行聚类,每一类的聚类正确度大于90%;对于缺失数据的Wisconsin Breast Cancer数据,错分率为4.72%。该聚类方法在性能上比经典聚类算法有所改进,具有更快的收敛速度以及较高的准确度。仿真实验的结果证实了修正核聚类方法的可行性和有效性。
文摘为了实现刀具图像的轮廓跟踪,提取轮廓点,采用Matlab.NET component与Mi-crosoft Visual Studio C Sharp混和编程技术,并运用模糊C均值聚类(Fuzzy C Mean cluste-ring,FCM)算法对图像进行处理,返回分割阈值,实现对灰度图像的分割.同时为避免出现轮廓断点,引入编码技术,再经过8邻域搜索目标轮廓,最终完成刀具图像轮廓跟踪.与微分算子的边缘检测对比,本文算法利用FCM分割图像,又引入编码技术,实现轮廓连续的跟踪检测,该方法分割效果较好,界面编写简单,运行简便.
