111   Artículos

 
en línea
Jingyi Hu, Junfeng Guo, Zhiyuan Rui and Zhiming Wang    
To solve the problem that noise seriously affects the online monitoring of parts signals of outdoor machinery, this paper proposes a signal reconstruction method integrating deep neural network and compression sensing, called ADMM-1DNet, and gives a deta... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Haoyu Lin, Pengkun Quan, Zhuo Liang, Dongbo Wei and Shichun Di    
In the context of automatic charging for electric vehicles, collision localization for the end-effector of robots not only serves as a crucial visual complement but also provides essential foundations for subsequent response design. In this scenario, dat... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Zhenyu Yin, Feiqing Zhang, Guangyuan Xu, Guangjie Han and Yuanguo Bi    
Confronting the challenge of identifying unknown fault types in rolling bearing fault diagnosis, this study introduces a multi-scale bearing fault diagnosis method based on transfer learning. Initially, a multi-scale feature extraction network, MBDCNet, ... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Zhuofan Xu, Jing Yan, Guoqing Sui, Yanze Wu, Meirong Qi, Zilong Zhang, Yingsan Geng and Jianhua Wang    
High-voltage circuit breakers (HVCBs) handle the important tasks of controlling and safeguarding electricity networks. In the case of insufficient data samples, improving the accuracy of the traditional HVCB mechanical fault diagnosis method is difficult... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
M. Domaneschi, R. Cucuzza, L. Sardone, S. Londoño Lopez, M. Movahedi and G. C. Marano    
Random vibration analysis is a mathematical tool that offers great advantages in predicting the mechanical response of structural systems subjected to external dynamic loads whose nature is intrinsically stochastic, as in cases of sea waves, wind pressur... ver más
Revista: Computation    Formato: Electrónico

 
en línea
Ling Zhao, Xin Chi, Pan Li and Jiawei Ding    
A rolling bearing vibration signal fault feature enhancement method based on adaptive complete ensemble empirical mode decomposition with adaptive noise algorithm (CEEMDAN) and maximum correlated kurtosis deconvolution (MCKD) is proposed to address the i... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Cheng-Jian Lin, Chun-Hui Lin and Frank Lin    
The spindle of a machine tool plays a key role in machining because the wear of a spindle might result in inaccurate production and decreased productivity. To understand the condition of a machine tool, a vector-based convolutional fuzzy neural network (... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Mengting Zou, Jun Ma, Xin Xiong and Rong Li    
To investigate the vibration properties in healthy and fault conditions of planetary gearboxes, a phenomenological model is constructed to present the vibration spectrum structure. First, the effects of the base deflection of the gear fillet and the flex... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Saige Lv and Xiong Hu    
In order to solve the problems of subjectivity in the extraction of traditional degradation features and incomplete degradation information contained in a single sensor signal, a performance degradation assessment and abnormal health status detection met... ver más
Revista: Journal of Marine Science and Engineering    Formato: Electrónico

 
en línea
Lijun Zhang, Yuejian Zhang and Guangfeng Li    
Rolling bearings and gears are important components of rotating machinery. Their operating condition affects the operation of the equipment. Fault in the accessory directly leads to equipment downtime or a series of adverse reactions in the system, which... ver más
Revista: Algorithms    Formato: Electrónico

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