scholarly journals Random Fuzzy Extension of the Universal Generating Function Approach for the Reliability Assessment of Multi-State Systems Under Aleatory and Epistemic Uncertainties

2014 ◽  
Vol 63 (1) ◽  
pp. 13-25 ◽  
Author(s):  
Yan-Fu Li ◽  
Yi Ding ◽  
Enrico Zio
Keyword(s):  
Generating Function ◽  
Reliability Assessment ◽  
Function Approach ◽  
Universal Generating Function

Reliability Assessment for Uncertain Multi-state Systems: An Extension of Fuzzy Universal Generating Function

2018 ◽  
Vol 21 (3) ◽  
pp. 945-953 ◽  
Author(s):  
Wenjie Dong ◽  
Sifeng Liu ◽  
Qin Zhang ◽  
Rafal Mierzwiak ◽  
Zhigeng Fang ◽  
...  
Keyword(s):  
Generating Function ◽  
Reliability Assessment ◽  
Universal Generating Function

scholarly journals Reliability assessment of wind turbine generators by fuzzy universal generating function

2021 ◽  
Vol 23 (2) ◽  
pp. 308-314
Author(s):  
Tudi Huang ◽  
Tangfan Xiahou ◽  
Yan-Feng Li ◽  
Hua-Ming Qian ◽  
Yu Liu ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Generating Function ◽  
Fuzzy Numbers ◽  
Epistemic Uncertainty ◽  
Reliability Assessment ◽  
System Structure ◽  
Limited Data ◽  
Turbine Generators ◽  
Wind Turbine Generators ◽  
Universal Generating Function

Wind power has been widely used in the past decade because of its safety and cleanness. Double fed induction generator (DFIG), as one of the most popular wind turbine generators, suffers from degradation. Therefore, reliability assessment for this type of generator is of great significance. The DFIG can be characterized as a multi-state system (MSS) whose components have more than two states. However, due to the limited data and/or vague judgments from experts, it is difficult to obtain the accurate values of the states and thus it inevitably contains epistemic uncertainty. In this paper, the fuzzy universal generating function (FUGF) method is utilized to conduct the reliability assessment of the DFIG by describing the states using fuzzy numbers. First, the fuzzy states of the DFIG system’s components are defined and the entire system state is calculated based the system structure function. Second, all components’ states are determined as triangular fuzzy numbers (TFN) according to experts’ experiences. Finally, the reliability assessment of the DFIG based on the FUGF is conducted.

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