SOFT-COMPUTING TECHNIQUES FOR FAULT DIAGNOSIS OF ELECTRICAL DRIVES
Journal Title: International Journal of Engineering Sciences & Research Technology - Year 0, Vol 5, Issue 1
Abstract
A method of using fuzzy logic to interpret current sensors signal of induction motor for its stator condition monitoring was presented. Correctly processing theses current signals and inputting them to a fuzzy decision system achieved high diagnosis accuracy. There is most likely still room for improvement by using an intelligent means of optimization. Fault Detection Scheme using Neuro-Fuzzy Approach ANFIS had gained popularity over other techniques due to its knowledge extraction feasibility, domain partitioning, rule structuring and modifications. The artificial neural network (ANN) has the capability of solving the motor monitoring and fault detection problem using an inexpensive, reliable procedure. However, it does not provide heuristic reasoning about the fault detection process. On the other hand, fuzzy logic can easily provide heuristic reasoning, while being difficult to provide exact solutions. By merging the positive features of ANN and fuzzy logic, a simple noninvasive fault detection technique is developed. By using a hybrid, supervised learning algorithm, ANFIS can construct an input-output mapping. The supervised learning (gradient descent) algorithm is used here to train the weights to minimize the errors.
Authors and Affiliations
Sulekha Shukla
Keywords
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- EP ID EP112093
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