The Experts below are selected from a list of 21366 Experts worldwide ranked by ideXlab platform
Mengchu Zhou - One of the best experts on this subject based on the ideXlab platform.
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g image segmentation similarity preserving fuzzy c means with spatial information constraint in Wavelet Space
IEEE Transactions on Fuzzy Systems, 2020Co-Authors: Cong Wang, Witold Pedrycz, Mengchu ZhouAbstract:G-images refer to image data defined on irregular graph domains. This work elaborates a similarity-preserving Fuzzy C-Means (FCM) algorithm for G-image segmentation and aims to develop techniques and tools for segmenting G-images. To preserve the membership similarity between an arbitrary image pixel and its neighbors, a Kullback-Leibler divergence term on membership partition is introduced as a part of FCM. As a result, similarity-preserving FCM is developed by considering spatial information of image pixels for its robustness enhancement. Due to superior characteristics of a Wavelet Space, the proposed FCM is performed in this Space rather than Euclidean one used in conventional FCM to secure its high robustness. Experiments on synthetic and real-world G-images demonstrate that it indeed achieves higher robustness and performance than the state-of-the-art FCM algorithms. Moreover, it requires less computation than most of them.
Cong Wang - One of the best experts on this subject based on the ideXlab platform.
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g image segmentation similarity preserving fuzzy c means with spatial information constraint in Wavelet Space
IEEE Transactions on Fuzzy Systems, 2020Co-Authors: Cong Wang, Witold Pedrycz, Mengchu ZhouAbstract:G-images refer to image data defined on irregular graph domains. This work elaborates a similarity-preserving Fuzzy C-Means (FCM) algorithm for G-image segmentation and aims to develop techniques and tools for segmenting G-images. To preserve the membership similarity between an arbitrary image pixel and its neighbors, a Kullback-Leibler divergence term on membership partition is introduced as a part of FCM. As a result, similarity-preserving FCM is developed by considering spatial information of image pixels for its robustness enhancement. Due to superior characteristics of a Wavelet Space, the proposed FCM is performed in this Space rather than Euclidean one used in conventional FCM to secure its high robustness. Experiments on synthetic and real-world G-images demonstrate that it indeed achieves higher robustness and performance than the state-of-the-art FCM algorithms. Moreover, it requires less computation than most of them.
Fengling Zhang - One of the best experts on this subject based on the ideXlab platform.
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fault diagnosis of intershaft bearings using fusion information exergy distance method
Shock and Vibration, 2018Co-Authors: Jing Tian, Chengwei Fei, Ming Zhao, Fengling Zhang, Zhi WangAbstract:For the fault diagnosis of intershaft bearings, the fusion information exergy distance method (FIEDM) is proposed by fusing four information exergies, such as singular spectrum exergy, power spectrum exergy, Wavelet energy spectrum exergy, and Wavelet Space spectrum exergy, which are extracted from acoustic emission (AE) signals under multiple rotational speeds and multimeasuring points. The theory of FIEDM is investigated based on four information exergy distances under multirotational speeds. As for rolling bearings, four faults and one normal condition are simulated on a birotor test rig to collect the AE signals, in which the four faults are inner ring fault, outer ring fault, rolling element fault, and inner race-rolling element coupling fault. The faults of the intershaft bearings are analyzed and diagnosed by using the FIEDM. From the investigation, it is demonstrated that the faults of the intershaft bearings are accurately diagnosed and identified, and the FIEDM is effective for the analysis and diagnosis of intershaft bearing faults. Furthermore, the fault diagnosis precision of intershaft bearings becomes higher with increasing rotational speed.
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fusion information entropy method of rolling bearing fault diagnosis based on n dimensional characteristic parameter distance
Mechanical Systems and Signal Processing, 2017Co-Authors: Jiaoyue Guan, Jing Tian, Chengwei Fei, Fengling ZhangAbstract:Abstract To monitor rolling bearing operating status with casings in real time efficiently and accurately, a fusion method based on n -dimensional characteristic parameters distance ( n -DCPD) was proposed for rolling bearing fault diagnosis with two types of signals including vibration signal and acoustic emission signals. The n -DCPD was investigated based on four information entropies (singular spectrum entropy in time domain, power spectrum entropy in frequency domain, Wavelet Space characteristic spectrum entropy and Wavelet energy spectrum entropy in time-frequency domain) and the basic thought of fusion information entropy fault diagnosis method with n -DCPD was given. Through rotor simulation test rig, the vibration and acoustic emission signals of six rolling bearing faults (ball fault, inner race fault, outer race fault, inner-ball faults, inner-outer faults and normal) are collected under different operation conditions with the emphasis on the rotation speed from 800 rpm to 2000 rpm. In the light of the proposed fusion information entropy method with n -DCPD, the diagnosis of rolling bearing faults was completed. The fault diagnosis results show that the fusion entropy method holds high precision in the recognition of rolling bearing faults. The efforts of this study provide a novel and useful methodology for the fault diagnosis of an aeroengine rolling bearing.
Chengwei Fei - One of the best experts on this subject based on the ideXlab platform.
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fault diagnosis of intershaft bearings using fusion information exergy distance method
Shock and Vibration, 2018Co-Authors: Jing Tian, Chengwei Fei, Ming Zhao, Fengling Zhang, Zhi WangAbstract:For the fault diagnosis of intershaft bearings, the fusion information exergy distance method (FIEDM) is proposed by fusing four information exergies, such as singular spectrum exergy, power spectrum exergy, Wavelet energy spectrum exergy, and Wavelet Space spectrum exergy, which are extracted from acoustic emission (AE) signals under multiple rotational speeds and multimeasuring points. The theory of FIEDM is investigated based on four information exergy distances under multirotational speeds. As for rolling bearings, four faults and one normal condition are simulated on a birotor test rig to collect the AE signals, in which the four faults are inner ring fault, outer ring fault, rolling element fault, and inner race-rolling element coupling fault. The faults of the intershaft bearings are analyzed and diagnosed by using the FIEDM. From the investigation, it is demonstrated that the faults of the intershaft bearings are accurately diagnosed and identified, and the FIEDM is effective for the analysis and diagnosis of intershaft bearing faults. Furthermore, the fault diagnosis precision of intershaft bearings becomes higher with increasing rotational speed.
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fusion information entropy method of rolling bearing fault diagnosis based on n dimensional characteristic parameter distance
Mechanical Systems and Signal Processing, 2017Co-Authors: Jiaoyue Guan, Jing Tian, Chengwei Fei, Fengling ZhangAbstract:Abstract To monitor rolling bearing operating status with casings in real time efficiently and accurately, a fusion method based on n -dimensional characteristic parameters distance ( n -DCPD) was proposed for rolling bearing fault diagnosis with two types of signals including vibration signal and acoustic emission signals. The n -DCPD was investigated based on four information entropies (singular spectrum entropy in time domain, power spectrum entropy in frequency domain, Wavelet Space characteristic spectrum entropy and Wavelet energy spectrum entropy in time-frequency domain) and the basic thought of fusion information entropy fault diagnosis method with n -DCPD was given. Through rotor simulation test rig, the vibration and acoustic emission signals of six rolling bearing faults (ball fault, inner race fault, outer race fault, inner-ball faults, inner-outer faults and normal) are collected under different operation conditions with the emphasis on the rotation speed from 800 rpm to 2000 rpm. In the light of the proposed fusion information entropy method with n -DCPD, the diagnosis of rolling bearing faults was completed. The fault diagnosis results show that the fusion entropy method holds high precision in the recognition of rolling bearing faults. The efforts of this study provide a novel and useful methodology for the fault diagnosis of an aeroengine rolling bearing.
Witold Pedrycz - One of the best experts on this subject based on the ideXlab platform.
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g image segmentation similarity preserving fuzzy c means with spatial information constraint in Wavelet Space
IEEE Transactions on Fuzzy Systems, 2020Co-Authors: Cong Wang, Witold Pedrycz, Mengchu ZhouAbstract:G-images refer to image data defined on irregular graph domains. This work elaborates a similarity-preserving Fuzzy C-Means (FCM) algorithm for G-image segmentation and aims to develop techniques and tools for segmenting G-images. To preserve the membership similarity between an arbitrary image pixel and its neighbors, a Kullback-Leibler divergence term on membership partition is introduced as a part of FCM. As a result, similarity-preserving FCM is developed by considering spatial information of image pixels for its robustness enhancement. Due to superior characteristics of a Wavelet Space, the proposed FCM is performed in this Space rather than Euclidean one used in conventional FCM to secure its high robustness. Experiments on synthetic and real-world G-images demonstrate that it indeed achieves higher robustness and performance than the state-of-the-art FCM algorithms. Moreover, it requires less computation than most of them.
