Article
Engineering, Multidisciplinary
Yuanhang Sun, Jianbo Yu
Summary: This paper proposes a novel fault feature extraction method, adaptive adjacent signal difference lasso (AdaASDL), based on a new sparse representation for bearing fault diagnosis. AdaASDL model enhances the sparsity of signal amplitude and adjacent signal difference through sparse regularization terms, and the regularization parameter is adaptively set using a weighted method. Compared with other state-of-the-art methods, AdaASDL demonstrates better denoising performance and superiority in bearing fault diagnosis.
Article
Chemistry, Analytical
Long Zhang, Lijuan Zhao, Chaobing Wang, Qian Xiao, Haoyang Liu, Hao Zhang, Yanqing Hu
Summary: This paper proposes a novel model, IACMDSR, for compound fault diagnosis of bearings in noise-heavy vibration signals. The model utilizes improved adaptive chirp mode decomposition and sparse representation to extract multiple features embedded in the signals. Simulation and experimental results demonstrate that the developed IACMDSR model outperforms other models and shows satisfactory capability in practical applications.
Article
Engineering, Mechanical
Xiaoxin Liu, Yungong Li, Minghao Sun, Sun Zhongqiu, Jingye Zang
Summary: Rolling bearing is important in rotary machines. Integrating two-channel signals from bearing can improve the comprehensiveness and accuracy of fault diagnosis. A DLNON model, simulating the human binaural auditory system, is proposed for feature extraction and diagnosis, effectively extracting fault features and distinguishing fault types and severity.
NONLINEAR DYNAMICS
(2023)
Article
Engineering, Electrical & Electronic
Kexin Yin, Chunjun Chen, Beice Luo, Ji Deng
Summary: In this article, a novel deep domain adaptation approach called fault feature proxy transfer (FFPT) is implemented to facilitate knowledge transfer across different operating condition domains in the presence of imbalanced class distribution. The proposed approach achieves high accuracy and robustness in fault diagnosis of rotary machinery.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Electrical & Electronic
Jing Tian, Dong Wang, Liang Chen, Zhongkui Zhu, Changqing Shen
Summary: This article proposes a novel method for fault diagnosis, which achieves stable and accurate diagnosis results in different working conditions by dynamically adjusting the importance of marginal and conditional distributions. Experimental results demonstrate the effectiveness and usability of the proposed method.
IEEE SENSORS JOURNAL
(2022)
Article
Automation & Control Systems
Zhuang Ye, Jianbo Yu
Summary: A novel deep neural network called MWMNet is proposed in this article for extracting impulses from vibration signals and performing fault diagnosis. MWMNet utilizes a smoothly embedded morphological layer to filter out noise and employs multiple branches with different scales and adaptive weighted fusion to extract impulse signals. Experimental results demonstrate that MWMNet can learn fault-related features and filter out noise, outperforming other DNN models in fault diagnosis performance.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Engineering, Electrical & Electronic
Xia He, Qingsong Zhang, Jianming Ding, Wentao Zhao
Summary: This study proposes a method to solve the difficulty of extracting weaker fault features of wheelset bearings under time-varying speed conditions. By extracting signals in the angle domain and using a more robust impulsiveness index and a more accurate optimal resonance band (ORB) localization method, a fault diagnosis framework for wheelset bearings under time-varying speed conditions is developed.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Civil
Jinhai Wang, Jianwei Yang, Yongliang Bai, Yue Zhao, Yuping He, Dechen Yao
Summary: A dynamic model with inner/outer races faults of axlebox bearing for railway vehicles was established and validated with experimental data. Vibration features showed different sensitivities for inner and outer race fault identification, with peak-to-peak value (PPV) performing best for inner race fault and skewness value (SV) for outer race fault at most severe fault cases. This research provides meaningful guidance for accurate diagnosis of axlebox bearing faults in railway vehicles.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART F-JOURNAL OF RAIL AND RAPID TRANSIT
(2021)
Article
Engineering, Mechanical
Jianwei Yang, Yue Zhao, Jinhai Wang, Changdong Liu, Yongliang Bai
Summary: This paper develops a railway vehicle model equipped with a helical gear system and validates it using experimental data. The study finds that wheel flat has a significant impact on the vibration and stability of the gear system.
ENGINEERING FAILURE ANALYSIS
(2022)
Article
Engineering, Electrical & Electronic
Andrei S. Maliuk, Zahoor Ahmad, Jong-Myon Kim
Summary: This paper proposes a framework aimed at improving the accuracy of bearing-fault diagnosis. The framework utilizes a hybrid feature-selection method based on Wrapper-WPT. It decomposes the vibration signal using Wavelet Packet Transform and extracts time and frequency domain features. The features are then selected using the Boruta algorithm, and a Subspace k-NN is used for bearing fault diagnosis. The proposed method shows higher classification performance compared to other state-of-the-art methods.
Article
Physics, Multidisciplinary
Runtao Sun, Jianwei Yang, Dechen Yao, Jinhai Wang
Summary: This study proposes an adaptive technique for defect identification based on multipoint optimal minimum entropy deconvolution and Ramanujan subspace decomposition. The technique successfully addresses the issues of conventional signal decomposition and subspace decomposition methods when dealing with vibration signals under loud noise. Through simulation and experimentation, the technique is proven to work well in identifying bearing faults.
Article
Mathematics
Chun-Yao Lee, Truong-An Le, Chung-Yao Chang
Summary: This paper proposes a model for detecting faulty bearings using the technique for order of preference by similarity to the ideal solution (TOPSIS) for feature prioritization. The model includes feature extraction, feature selection, and classification steps. It applies variational mode decomposition (VMD) and fast Fourier transform (FFT) for feature extraction and the TOPSIS method for feature selection. The model is evaluated using two bearing datasets and shows promising results compared to other models.
Article
Energy & Fuels
Zhenhao Tang, Mengjiao Wang, Tinghui Ouyang, Fei Che
Summary: Diagnosis of bearing faults is of significant importance in wind turbine maintenance. This paper proposes a feature extraction method for wind turbine bearing fault diagnosis, aiming to improve accuracy. The method involves three steps: time-domain feature extraction using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), deep frequency-domain features extraction using fast Fourier transform (FFT), and optimal feature subset selection using recursive feature elimination (RFE) combined with chi-square test. The proposed method is tested on industrial data from Case Western Reserve University (CWRU) and Jiangxi wind farm, and the results demonstrate its effectiveness and applicability in real wind turbine bearing fault diagnosis.
Article
Automation & Control Systems
Dongdong Liu, Lingli Cui, Weidong Cheng
Summary: Rotating machinery fault diagnosis under nonstationary conditions commonly relies on manual analysis of vibration signals' frequency spectrums or time-frequency representations. However, expert experience heavily determines the results of these methods, and identifying the frequency content becomes difficult due to the intricate interference frequency components caused by complex modulation characteristics and operation conditions. This article investigates a novel intelligent fault diagnosis method for rolling bearings under nonstationary conditions. A flexible generalized demodulation method is proposed, which overcomes the effects of operation conditions on demodulation spectrums. Based on this, a fault feature extraction method is further proposed to capture useful fault information. Experimental results show that the proposed method can automatically recognize health conditions that cannot be manually identified by demodulation spectrums due to interference frequency components and it is more adaptive to new operation conditions because of its definite physical meaning.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Automation & Control Systems
Tinggui Chen, Dejie Yu
Summary: This article proposes a novel method to diagnose bearing faults using acoustic metamaterials, which enhances the resonance frequency band to extract fault features. Compared to conventional denoising techniques, this method shows superior performance in low signal to noise ratios.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Biochemistry & Molecular Biology
Yangyang Cai, Dezhong Lv, Donghao Li, Jiaqi Yin, Yingying Ma, Ya Luo, Limei Fu, Na Ding, Yongsheng Li, Zhenwei Pan, Xia Li, Juan Xu
Summary: IEAtlas is a valuable resource for investigating the immunogenic capacity of non-canonical epitopes and their potential as therapeutic cancer vaccines.
NUCLEIC ACIDS RESEARCH
(2023)
Article
Medicine, Research & Experimental
Zan-Xiong Chen, Hong-Qian Liu, Zhen-Hua Wu, Jun-Lian He, Hao-Jie Zhong
Summary: This study found that the frequency of circulating ILC3s is elevated in patients with HUA and is positively correlated with serum uric acid and creatinine levels. Although there is no significant difference in plasma IL-17A concentration between HUA patients and healthy controls, positive correlations between plasma IL-17A and serum uric acid levels and circulating ILC3 frequency were observed in HUA patients.
ADVANCES IN CLINICAL AND EXPERIMENTAL MEDICINE
(2023)
Letter
Otorhinolaryngology
Thomas J. Lepley, Zhenxing Wu, Zachary T. Root, Barak M. Spector, Robbie J. Chapman, Aspen R. Schneller, Kathleen Kelly, Bradley A. Otto, Kai Zhao
INTERNATIONAL FORUM OF ALLERGY & RHINOLOGY
(2023)
Book Review
Communication
Yongjian Li
NEW MEDIA & SOCIETY
(2023)
Article
Demography
Yongjian Li, Amanda Alencar
Summary: Over the past four decades, there has been a growing trend of Chinese ageing migrants moving from the North to the South during winter and returning during summer, similar to the migration patterns of snowbirds. The flexibilization of the household registration system, urbanization, and infrastructure development have led to a new spatial pattern of mobility and the search for new lifestyles among elderly populations, supported by ICT. This study explores the use of smartphones and social media by elderly Houniao participants during their seasonal migration to Southern cities in China.
JOURNAL OF ETHNIC AND MIGRATION STUDIES
(2023)
Article
Chemistry, Multidisciplinary
Ying Zhang, Yi-yuan Zhang, Zhen-wei Pan, Qing-qi Li, Li-hua Sun, Xin Li, Man-yu Gong, Xue-wen Yang, Yan-ying Wang, Hao-dong Li, Li-na Xuan, Ying-chun Shao, Meng-meng Li, Ming-yu Zhang, Qi Yu, Zhange Li, Xiao-fang Zhang, Dong-hua Liu, Yan-meng Zhu, Zhong-yue Tan, Yuan-yuan Zhang, Yun-qi Liu, Yong Zhang, Lei Jiao, Bao-feng Yang
Summary: This study found that GDF11 can accelerate the healing of diabetic wounds by promoting neovascularization and regeneration of skin tissues through cellular and molecular mechanisms. Topical application of GDF11 stimulates the mobilization of endothelial progenitor cells and enhances neovascularization through the HIF-1 alpha-VEGF/SDF-1 alpha pathway.
ACTA PHARMACOLOGICA SINICA
(2023)
Article
Optics
Jing Bai, Xuan Liu, Cheng-xian Ge, Zhen-sen Wu, Xiao-xiao Zhang
Summary: We studied the optical trapping force exerted on non-uniform chiral stratified spheres using a high-order Bessel beam. By comparing with existing reference, we validated the present theories. Numerical simulations considering various parameters showed that different chirality distributions significantly affect the trapping characteristics, and a stable three-dimensional capture can be achieved by selecting appropriate incident beam and particle parameters. These theoretical investigations offer an analytical method for understanding the interaction between light and more complex stratified chiral cells, thus providing an encouraging approach for designing better optical manipulation systems.
Correction
Multidisciplinary Sciences
Robert. D. D. Bell, Ethan. A. A. Winkler, Itender Singh, Abhay. P. P. Sagare, Rashid Deane, Zhenhua Wu, David. M. M. Holtzman, Christer Betsholtz, Annika Armulik, Jan Sallstrom, Bradford. C. C. Berk, Berislav. V. V. Zlokovic
Article
Optics
Shaojie Chang, Zhenhua Wu, Diwei Liu, Renbin Zhong, Zhaoyun Duan, Yanyu Wei, Yubin Gong, Min Hu
Summary: In this study, we propose a novel approach to enhance the second harmonic of electron beams and increase the output power at higher frequencies. By using a planar grating for fundamental modulation and a transmission grating for harmonic coupling, the proposed structure achieves a high power output of the second harmonic signal. Computational investigations demonstrate that this configuration can generate a 0.202 THz signal with an output power of 4.59 W at an electron beam density of 50 A/cm2 and a voltage of 31.5 kV. The reduced current density in the G-band has significant implications for the advancement of terahertz vacuum devices.
Article
Multidisciplinary Sciences
Zhenxing Wu, Jike Wang, Hongyan Du, Dejun Jiang, Yu Kang, Dan Li, Peichen Pan, Yafeng Deng, Dongsheng Cao, Chang-Yu Hsieh, Tingjun Hou
Summary: The authors propose a substructure mask explanation method to address the challenge of explaining the predictions made by neural networks in molecular property prediction. This method provides an interpretation consistent with the understanding of chemists. By applying this method, the authors elucidate how neural networks learn to predict various properties for small molecules and empower chemists to confidently analyze structure-activity relationships.
NATURE COMMUNICATIONS
(2023)
Article
Chemistry, Physical
Meng Li, Hao Chen, Can Guo, Shangshu Qian, Hongpeng Li, Zhenzhen Wu, Chao Xing, Pan Xue, Shanqing Zhang
Summary: To address the issues of lithium-sulfur batteries, a dual interfacial engineering strategy is proposed. For the cathode, iminated polyaniline (iPANI) is used to achieve energetic and morphological engineering. For the anode, the iPANI@rGO-CNTs composite facilitates uniform deposition of lithium-ions, preventing dendrite growth. With the synergic effects of iPANI@rGO-CNTs nanoreactors, the prepared LSBs exhibit excellent rate capability and cycling life, potentially leading to commercialization.
ADVANCED ENERGY MATERIALS
(2023)
Review
Nanoscience & Nanotechnology
Yuhui Tian, Daijie Deng, Li Xu, Meng Li, Hao Chen, Zhenzhen Wu, Shanqing Zhang
Summary: A sustainable and cost-effective production of H2O2 via electrochemical oxygen reduction reaction is highly desired for various applications. Significant progress has been made in catalyst design and innovative cell designs for electrocatalytic production of H2O2. This review summarizes recent advances, including mechanistic explorations, theoretical computations, experimental evaluations, and perspectives on addressing remaining challenges.
NANO-MICRO LETTERS
(2023)
Article
Chemistry, Multidisciplinary
Mengting Zheng, Juncheng Wang, Shangshu Qian, Qiang Sun, Hao Chen, Liang Zhang, Zhenzhen Wu, Shanqing Zhang, Tiefeng Liu
Summary: A one-step water-based recycling process is proposed to recycle and regenerate graphite anode materials from spent lithium-ion batteries (LIBs). The regenerated graphite with expanded interlayer spacing and oxygen-containing groups exhibits excellent performance in graphite dual-ion batteries (GDIBs). This waste-to-resource strategy provides a low-cost and sustainable recycling pathway for spent LIBs and enables the sustainable manufacturing of GDIBs.
ACS SUSTAINABLE CHEMISTRY & ENGINEERING
(2023)
Article
Chemistry, Analytical
Man Wu, Yuhang Huang, Yaru Huang, Hua Wang, Min Li, Yang Zhou, Hui Zhao, Yuwei Lan, Zhenhua Wu, Chunping Jia, Shilun Feng, Jianlong Zhao
Summary: This study developed a microfluidic chip integrated with magnetic control technology for nucleic acid extraction, purification and amplification. The chip can automatically extract nucleic acid from multiple samples within 20 min and can be directly used for amplification. Furthermore, a multi-target detection chip was developed based on the droplet magnetic-controlled microfluidic chip, which successfully detected macrolides resistance mutations and mycoplasma pneumoniae gene in clinical samples, providing the possibility for future detection of multiple pathogens.
ANALYTICA CHIMICA ACTA
(2023)
Article
Biochemistry & Molecular Biology
Zhenhua Wu, Xiaoli Zhu, Anjin Hong, Guanghui He, Zheng Wang, Qingyan Xu, Zhiyu Hu, Xiaobing Wu, Yuezhou Wang, Qiufang Chen, Xilin Zhao, Li Li, Xianming Deng
Summary: Inspired by the structural insights from the analysis of lefamulin's cocrystal structure with S. aureus ribosomes, a series of novel pleuromutilin derivatives were designed and synthesized. The study revealed that derivatives with urea in the meta position of phenylene sulfide showed optimal antibacterial activities. Among them, 21h exhibited the most potent activity against MRSA and clinical AMR Gram-positive bacteria, with low resistance frequency and prolonged Post-Antibiotic Effect. It also showed promising antibacterial activity in vivo, making it a potential lead for new antibiotics against Gram-positive pathogens, especially for AMR bacteria.
BIOORGANIC CHEMISTRY
(2023)