Fault Diagnosis of Power Transformers Using SVM/ANN with Clonal Selection Algorithm for Features and Kernel Parameters Selection

2006 ◽  
Author(s):  
Ming-Yuan Cho ◽  
Tsair-Fwu Lee ◽  
Shih-Wei Kau ◽  
Chin-Shiuh Shieh ◽  
Chao-Ji Chou
Keyword(s):  
Fault Diagnosis ◽  
Clonal Selection ◽  
Power Transformers ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Parameters Selection ◽  
Kernel Parameters

Clonal fuzzy intelligent system for fault diagnosis of cracked beam

2017 ◽  
Vol 27 (6) ◽  
pp. 840-858 ◽  
Author(s):  
Dayal R Parhi ◽  
Sasmita Sahu
Keyword(s):  
Fuzzy Logic ◽  
Fault Diagnosis ◽  
Natural Frequencies ◽  
Intelligent System ◽  
Clonal Selection ◽  
Crack Depth ◽  
Research Work ◽  
Detection Methods ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm

Cracks, faults, or damages are serious threat to the current and future performance of the system. For decades, research work is being carried out on the dynamic behavior of the structural elements for fault diagnosis. The modern types of damage detection methods use the dynamic response from the signals of the beams. In this article, a robust fault diagnostic tool based on clonal selection algorithm and fuzzy logic has been proposed. Theoretical and finite element analyses are done to model the crack and to find the effect of the presence of cracks changes on the vibrational characteristic (natural frequencies) of a fixed–fixed beam. The inputs to (Clonal Selection Algorithm-Fuzzy Logic) system are the first three relative natural frequencies and the outputs from the system are the relative crack depth and relative crack location. Experimental validations with the theoretical results have been carried out to check the robustness of the predicted algorithm.

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