Results 21 to 30 of about 122,093 (281)

Composite Fault Diagnosis of Rolling Bearings: A Feature Selection Approach Based on the Causal Feature Network

Applied Sciences, 2023
A rolling bearing is a complex system consisting of the inner race, outer race, rolling element, etc. The interaction of components may lead to composite faults.
Kuo Gao, Zongning Wu, Chongchong Yu, Mengxiong Li, Sihan Liu   +4 more
doaj   +1 more source

An improved Autogram and MOMEDA method to detect weak compound fault in rolling bearings

Mathematical Biosciences and Engineering, 2022
When weak compound fault occurs in rolling bearing, the faint fault features suffer from serious noise interference, and different type faults are coupled together, making it a great challenge to separate the fault features.
Xuyang Xie, Zichun Yang, Lei Zhang , Guoqing Zeng , Xuefeng Wang, Peng Zhang, Guobing Chen   +6 more
doaj   +1 more source

Mechanical Compound Fault Analysis Method Based on Shift Invariant Dictionary Learning and Improved FastICA Algorithm

Machines, 2021
For mechanical compound fault, it is of great significance to employ the vibration signal of a single-channel compound fault to analyze and realize the separation of multiple fault sources, which is essentially the problem of single-channel blind source ...
Haodong Yuan, Nailong Wu, Xinyuan Chen
doaj   +1 more source

Analysis of rotor winding interturn short circuits affected by the initial dynamic eccentricity in large synchronous condensers

IET Electric Power Applications, 2022
To investigate the effect of initial dynamic eccentricity on the rotor winding turn‐to‐turn short circuit (RWISC) in a large synchronous condenser, this paper uses the study object of a 300 MVar dual water intercooler condenser. Firstly, the air gap flux
Minghan Ma, Yuejia Hou, Yonggang Li, Pengkang He, Meng Jiang, Yucai Wu   +5 more
doaj   +1 more source

A Deep Multi-Label Learning Framework for the Intelligent Fault Diagnosis of Machines

IEEE Access, 2020
Deep learning has been applied in intelligent fault diagnosis of machines since it trains deep neural networks to simultaneously learn features and recognize faults.
Jianjun Shen, Shihao Li, Feng Jia, Hao Zuo, Junxing Ma   +4 more
doaj   +1 more source

Deep Decoupling Convolutional Neural Network for Intelligent Compound Fault Diagnosis

IEEE Access, 2019
Intelligent compound fault diagnosis of rotating machinery plays a crucial role for the security, high-efficiency, and reliability of modern manufacture machines, but identifying and decoupling the compound fault are still a great challenge.
Ruyi Huang, Yixiao Liao, Shaohui Zhang, Weihua Li   +3 more
doaj   +1 more source

Compound Faults Feature Extraction for Rolling Bearings Based on Parallel Dual-Q-Factors and the Improved Maximum Correlated Kurtosis Deconvolution

Applied Sciences, 2019
Vibration analysis is one of the main effective ways for rolling bearing fault diagnosis, and a challenge is how to accurately separate the inner and outer race fault features from noisy compound faults signals.
Lingli Cui, Jianxi Du, Na Yang, Yonggang Xu, Liuyang Song   +4 more
doaj   +1 more source

A New Method of Wheelset Bearing Fault Diagnosis

Entropy, 2022
During the movement of rail trains, trains are often subjected to harsh operating conditions such as variable speed and heavy loads. It is therefore vital to find a solution for the issue of rolling bearing malfunction diagnostics in such circumstances ...
Runtao Sun, Jianwei Yang, Dechen Yao, Jinhai Wang   +3 more
doaj   +1 more source

Interatomic potentials for atomistic simulations of the Ti-Al system [PDF]

, 2003
Semi-empirical interatomic potentials have been developed for Al, alpha-Ti, and gamma-TiAl within the embedded atomic method (EAM) by fitting to a large database of experimental as well as ab-initio data.
A. Girshick, A. Landa, A.B. Belonoshko, A.F. Voter, B. Meyer, C. Fu, C. Woodward, C. Woodward, C. Woodward, C.L. Fu, C.L. Fu, C.L. Fu, D. Chen, D. Farkas, D.T. Kulp, E. Hashimoto, F. Appel, F. Appel, F. Ercolessi, G. Lu, H. Schaefer, J. Ehmann, J. Wiezorek, J.H. Rose, J.P. Perdew, J.P. Perdew, J.P. Perdew, J.P. Simmons, J.P. Simmons, J.R. Fernandez, K. Tanaka, K. Tanaka, K.H. Westmacott, L. Hsiung, L.G. Ferreira, M. Aoki, M. Aoki, M. Asta, M. Hagen, M. Koppers, M. Yamaguchi, M. Zupan, M.I. Baskes, M.I. Baskes, M.I. Baskes, M.J. Elliot, M.J. Mehl, M.J. Mehl, M.S. Daw, M.S. Daw, M.S. Daw, M.W. Finnis, O.K. Anderson, O.L. Bacq, O.N. Mryasov, P. Hohenberg, P.A. Korzhavyi, P.B. Legrand, P.D. Desai, P.G. Patridge, R. Pasianot, R.A. Johnson, R.E. Watson, R.H. Rautioaho, R.M. Emrick, R.W. Balluffi, S.H. Wei, S.I. Rao, S.I. Rao, S.M. Foiles, S.M. Foiles, T. Hong, T. Hong, T. Ikeda, T. Wang, U. Brossmann, U. Hansen, V. Paidar, V. Paidar, V.O. Shestopal, W. Kohn, W.J. Nellis, W.J. Zhang, W.R. Tyson, X.Y. Liu, Y. He, Y. Mishin, Y. Mishin, Y. Mishin, Y. Mishin, Y. Shirai, Y. Sun, Y. Wang, Y.W. Kim   +93 more
core   +1 more source

Application of Deep Neural Network in Gearbox Compound Fault Diagnosis

Energies, 2023
Gearbox fault diagnosis is vital to ensure the efficient operation of rotating machinery, and most gearbox faults in industrial production occur in the form of compound faults. To realize the diagnosis of compound faults in gearboxes at different speeds,
Xiangfeng Zhang, Qinghong Xu, Hong Jiang, Jun Li   +3 more
doaj   +1 more source