Open Access
| Issue |
MATEC Web Conf.
Volume 108, 2017
2017 International Conference on Mechanical, Aeronautical and Automotive Engineering (ICMAA 2017)
|
|
|---|---|---|
| Article Number | 10006 | |
| Number of page(s) | 6 | |
| Section | Control Theory and Technology | |
| DOI | https://doi.org/10.1051/matecconf/201710810006 | |
| Published online | 31 May 2017 | |
- C.X.Guo, Y.Wang, M.Wang, “Time-varying Outage Model for Transformers Representing Internal Latent fault,” Proc. Chin. Soc. Electrical. Eng. 33, 1 (2013) [Google Scholar]
- F.Jakob, J.J.Dukarm, “Thermodynamic Estimation of Transformer Fault Severity,” IEEE Trans. Power. Del. 30, 4, 1941–1948 (2015) [CrossRef] [Google Scholar]
- S.Bhowmick, S.Nandi, “Online Detection of an Interturn Winding Fault in Single-Phase Distribution Transformers Using a Terminal Measurement-Based Modeling Technique,” IEEE Trans. Power. Del, 30, 2, 1–9 (2015) [CrossRef] [Google Scholar]
- Z.X.Liu, B.Song, E.W.Li, et al. “Study of “code absence” in the IEC three-ratio method of dissolved gas analysis,” IEEE Electron. Insulat. Maga. 31, 6, 6–12 (2015) [CrossRef] [Google Scholar]
- P.V.B.Reddy, C.R.V.Kumar, K.H.Reddy, “Modeling of Surface Roughness in Wire Electrical Discharge Machining Using Artificial Neural Networks,” Int. J. Mech. Eng. &Rob. Res. 2, 1, 57–64 (2013) [Google Scholar]
- K.Bacha, S.Souahlia, M.Gossa, “Power transformer fault diagnosis based on dissolved gas analysis by support vector machine,”. Electron. Power. Sys. Res. 83, 1, 73–79 (2012) [Google Scholar]
- A.M.Shah, B.R.Bhalja, “Discrimination Between Internal Faults and Other Disturbances in Transformer Using the Support Vector Machine-Based Protection Scheme,” IEEE Trans. Power. Del. 28, 3, 1508–1515 (2013) [CrossRef] [Google Scholar]
- S.Hwang, M.K.Jeong, B.J.Yum, “Robust Relevance Vector Machine With Variational Inference for Improving Virtual Metrology Accuracy,” IEEE Trans. Semiconduct. Manu. 27, 1, 83–94 (2014) [CrossRef] [Google Scholar]
- M.E.Tipping, “Sparse bayesian learning and the relevance vector machine,” J. Mach. Learn. 1, 3, 211–244 (2001) [Google Scholar]
- J.Yin, Y.Zhu, Y.U.Guoqin. “Relevance vector machine and its application in transformer fault diagnosis,” Power. Sys. Techno. 32, 12, 2239–2247 (2012) [Google Scholar]
- T.Wang,H.Xu,J.Han, et al. “Cascaded H-Bridge Multilevel Inverter System Fault Diagnosis Using a PCA and Multi-class Relevance Vector Machine Approach,” IEEE Trans. Power. Electron. 30, 12, (2015) [Google Scholar]
- A.Y.SLam, V.O.KLi, “Chemical-reaction-inspired metaheuristic for optimization,” IEEE Trans. Evol. Comput. 14, 3, 381–399 (2010) [CrossRef] [Google Scholar]
- P.Sreeraj, T.Kannan, S.Maji, “Simulation and Parameter Optimization of GMAW Process Using Neural Networks and Particle Swarm Optimization Algorithm,” Int. J. Mech. Eng. &Rob. Res. 2, 1, 130–146 (2013) [Google Scholar]
- M.Adinarayana, G.Prasanthi, G.Krishnaiah, “Optimization for Surface Roughness, MRR, Power Consumption in Turning of En24 Alloy Steel Using Genetic Algorithm,” Int. J. Mech. Eng. &Rob. Res. 3, 1, 20–26 (2014) [Google Scholar]
- H.L.Ao, J.Cheng, J.Zheng, et al. “Roller Bearing Fault Diagnosis Method Based on Chemical Reaction Optimization and Support Vector Machine,” J. Comput. Civil. Eng. 29, 5, (2014) [Google Scholar]
- H.Duan, L.Gan, “Orthogonal Multiobjective Chemical Reaction Optimization Approach for the Brushless DC Motor Design,” IEEE T. Magn. 51, 1, 1–7 (2015) [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.