E3S Web Conf.
Volume 95, 2019The 3rd International Conference on Power, Energy and Mechanical Engineering (ICPEME 2019)
|Number of page(s)||5|
|Section||Materials Science and Engineering|
|Published online||13 May 2019|
- Lei Jiyao et al. Engineering signal processing technology. Chongqing: Chongqing University Press, 1990(in Chinese) [Google Scholar]
- Liu Ling. Zhangxi. Wang Linna. Current Situation and Development of Fault Diagnosis Technology [Journal Papers] Electronic Testing 2016 (2) (in Chinese) [Google Scholar]
- Zhang Xinhai.Lei Yong. Application of BP Neural Network in Mechanical Fault Diagnosis. Northwest Polytechnic University: School of Power and Energy, 2008(in Chinese) [Google Scholar]
- Dai Min. Xie Chun. Fault Diagnosis of Aircraft Engine Based on Fuzzy Weighted Nonferrous Network and BP Neural Network [Journal Papers] Science, Technology and Engineering. 2012(in Chinese) [Google Scholar]
- Li Ning, Lei Hongli, Han Jianding, Zhu Xihua. Research on Fault Reasoning Model of Aircraft Switched Reluctance Generation System [Journal Paper] Power Supply Technology. 2011 (5) (in Chinese) [Google Scholar]
- M. Demtgul, I.N. Tansel and S. Taskin.Fault diagnosis of pneumatic systems with artificial neural network Algorithms, Expert Systems with Applications, 2009, 36(7),10514–10519. [Google Scholar]
- S.C. Liu,S.Y. Liu,in:An Efficient Expert System for Machine Fault Diagnosis Source. International Journal of Advanced Manufacturing Technology. 2003, 21(9):69l–698. [Google Scholar]
- Autar R.K. An Automated Diagnostic Expert System for Diesel Engines. Journal of Engineering for Gas Turbines and Power,1996,118:673—679. [CrossRef] [Google Scholar]
- Davia J, Krivine J, Tiarri J et al. DIVA Recognition of Typical Situations for Turbine Generator Diagnosis. Journal of Intelligent and Robotic Systems,1988, 1:287–298. [Google Scholar]
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