Pinhole corrosion is difficult to discover through conventional ultrasonic guided waves inspection,particularly for micro-sized pinholes less than 1 mm in diameter.This study proposes a new micro-sized pinhole inspect...Pinhole corrosion is difficult to discover through conventional ultrasonic guided waves inspection,particularly for micro-sized pinholes less than 1 mm in diameter.This study proposes a new micro-sized pinhole inspection method based on segmented time reversal(STR)and high-order modes cluster(HOMC)Lamb waves.First,the principle of defect echo enhancement using STR is introduced.Conventional and STR inspection experiments were conducted on aluminum plates with a thickness of 3 mm and defects with different diameters and depths.The parameters of the segment window are discussed in detail.The results indicate that the proposed method had an amplitude four times larger than of conventional ultrasonic guided waves inspection method for pinhole defect detection and could detect micro-sized pinhole defects as small as 0.5 mm in diameter and 0.5 mm in depth.Moreover,the segment window location and width(5-10 times width of the conventional excitation signal)did not affect the detection sensitivity.The combination of low-power and STR is more conducive to detection in different environments,indicating the robustness of the proposed method.Compared with conventional ultrasonic guided wave inspection methods,the proposed method can detect much smaller defect echoes usually obscured by noise that are difficult to detect with a lower excitation power and thus this study would be a good reference for pinhole defect detection.展开更多
Modalclust is an R package which performs Hierarchical Mode Association Clustering (HMAC) along with its parallel implementation over several processors. Modal clustering techniques are especially designed to efficien...Modalclust is an R package which performs Hierarchical Mode Association Clustering (HMAC) along with its parallel implementation over several processors. Modal clustering techniques are especially designed to efficiently extract clusters in high dimensions with arbitrary density shapes. Further, clustering is performed over several resolutions and the results are summarized as a hierarchical tree, thus providing a model based multi resolution cluster analysis. Finally we implement a novel parallel implementation of HMAC which performs the clustering job over several processors thereby dramatically increasing the speed of clustering procedure especially for large data sets. This package also provides a number of functions for visualizing clusters in high dimensions, which can also be used with other clustering softwares.展开更多
Electric vehicle, as a clean energy industry, is an important branch. Electric vehicles not only are the energy of the electric user, but also can be used as mobile and distributed energy storage unit to the grid. As ...Electric vehicle, as a clean energy industry, is an important branch. Electric vehicles not only are the energy of the electric user, but also can be used as mobile and distributed energy storage unit to the grid. As a precondition of safety operation for power grid, studies of EVs’ charging load characteristics is also the theoretical basis of intelligent scheduling EVs charging orderly. This paper assesses the future of the electric vehicles development prospects, and secondly establishes a charging model of a single EV. Then, considering stochastic distribution of the initial state-of-charge (SOC0) and the arriving time of the vehicles, a cluster model of the charging station is proposed. Meanwhile, the paper from the types and charging mode of electric vehicles analyzes the behavior of EV. Finally, an example simulation is validated.展开更多
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
Michael K.Ng等人提出了新K-Modes聚类算法,它采用基于相对频率的启发式相异度度量方法,有效地提高了聚类精度,但不足的是在计算各类的属性分类值频率时假定类中样本对聚类的贡献相同。为了考虑类中样本对类中心的不同影响,提出一种粗糙...Michael K.Ng等人提出了新K-Modes聚类算法,它采用基于相对频率的启发式相异度度量方法,有效地提高了聚类精度,但不足的是在计算各类的属性分类值频率时假定类中样本对聚类的贡献相同。为了考虑类中样本对类中心的不同影响,提出一种粗糙K-Modes算法,通过粗糙集的上、下近似度量数据样本在类内的重要性程度,不仅可以获得比新K-Modes算法更好的聚类效果,而且可以在保证聚类效果的基础上降低白亮等人提出的基于粗糙集改进的K-Modes算法的计算复杂度。对几个UCI的数据集的测试实验结果显示出新算法的优良性能。展开更多
基金National Natural Science Foundation of China(Grant No.62071433)National Key R&D Program of China(Grant No.2022YFC3005002)。
文摘Pinhole corrosion is difficult to discover through conventional ultrasonic guided waves inspection,particularly for micro-sized pinholes less than 1 mm in diameter.This study proposes a new micro-sized pinhole inspection method based on segmented time reversal(STR)and high-order modes cluster(HOMC)Lamb waves.First,the principle of defect echo enhancement using STR is introduced.Conventional and STR inspection experiments were conducted on aluminum plates with a thickness of 3 mm and defects with different diameters and depths.The parameters of the segment window are discussed in detail.The results indicate that the proposed method had an amplitude four times larger than of conventional ultrasonic guided waves inspection method for pinhole defect detection and could detect micro-sized pinhole defects as small as 0.5 mm in diameter and 0.5 mm in depth.Moreover,the segment window location and width(5-10 times width of the conventional excitation signal)did not affect the detection sensitivity.The combination of low-power and STR is more conducive to detection in different environments,indicating the robustness of the proposed method.Compared with conventional ultrasonic guided wave inspection methods,the proposed method can detect much smaller defect echoes usually obscured by noise that are difficult to detect with a lower excitation power and thus this study would be a good reference for pinhole defect detection.
文摘Modalclust is an R package which performs Hierarchical Mode Association Clustering (HMAC) along with its parallel implementation over several processors. Modal clustering techniques are especially designed to efficiently extract clusters in high dimensions with arbitrary density shapes. Further, clustering is performed over several resolutions and the results are summarized as a hierarchical tree, thus providing a model based multi resolution cluster analysis. Finally we implement a novel parallel implementation of HMAC which performs the clustering job over several processors thereby dramatically increasing the speed of clustering procedure especially for large data sets. This package also provides a number of functions for visualizing clusters in high dimensions, which can also be used with other clustering softwares.
文摘Electric vehicle, as a clean energy industry, is an important branch. Electric vehicles not only are the energy of the electric user, but also can be used as mobile and distributed energy storage unit to the grid. As a precondition of safety operation for power grid, studies of EVs’ charging load characteristics is also the theoretical basis of intelligent scheduling EVs charging orderly. This paper assesses the future of the electric vehicles development prospects, and secondly establishes a charging model of a single EV. Then, considering stochastic distribution of the initial state-of-charge (SOC0) and the arriving time of the vehicles, a cluster model of the charging station is proposed. Meanwhile, the paper from the types and charging mode of electric vehicles analyzes the behavior of EV. Finally, an example simulation is validated.
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.
