Dynamic condition vector assessment method for electric power equipment of overall process: Novel and necessary technical approach of condition-based-assessment

Author(s):  
Yang Liu ◽  
Weidong Qi ◽  
Jingfeng Wu ◽  
Lu Pu ◽  
Sen Wang ◽  
...  
Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4237
Author(s):  
Takuma Tanaka ◽  
Togo Sugioka ◽  
Tatsuya Kobayashi ◽  
Ikuo Shohji ◽  
Yuya Shimada ◽  
...  

The effect of heat treatment on tensile and low cycle fatigue properties of the oxygen-free copper for electric power equipment was investigated. The heat treatment at 850 °C for 20 min, which corresponds to the vacuum brazing process, caused the grain growth and relaxation of strain by recrystallization, and thus, the residual stress in the oxygen-free copper was reduced. The tensile strength and 0.2% proof stress were decreased, and elongation was increased by the heat treatment accompanying recrystallization. The plastic strain in the heat-treated specimen was increased compared with that in the untreated specimen under the same stress amplitude condition, and thus, the low cycle fatigue life of the oxygen-free copper was degraded by the heat treatment. Striation was observed in the crack initiation area of the fractured surface in the case of the stress amplitude less than 100 MPa regardless of the presence of the heat treatment. With an increase in the stress amplitude, the river pattern and the quasicleavage fracture were mainly observed in the fracture surfaces of the untreated specimens, and they were observed with striations in the fracture surfaces of the heat-treated ones. The result of the electron backscattered diffraction (EBSD) analysis showed that the grain reference orientation deviation (GROD) map was confirmed to be effective to investigate the fatigue damage degree in the grain by low cycle fatigue. In addition, the EBSD analysis revealed that the grains were deformed, and the GROD value reached approximately 28° in the fractured areas of heat-treated specimens after the low cycle fatigue test.


2019 ◽  
Vol 2019 (16) ◽  
pp. 1774-1777 ◽  
Author(s):  
Ren Yang ◽  
Mengyuan Xu ◽  
Chen Guan ◽  
Jing Yan ◽  
Yingsan Geng

2013 ◽  
Vol 325-326 ◽  
pp. 692-696
Author(s):  
Da Peng Chai ◽  
Qiang Qiang Xue ◽  
Ling Mei Wang ◽  
Xing Yong Zhao

The substation electric power equipment condition monitoring is the basis of intelligent substation. This paper analyzes the composition of the substation electric power equipment condition monitoring system and monitoring parameters, and with the transformer condition monitoring as an example, this paper proposes fault diagnosis methods of electric power equipment using artificial neural network(ANN).


Sign in / Sign up

Export Citation Format

Share Document