Demagnetization Fault Detection on BLDC by Adaptive Neuro Fuzzy Inference System

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Kreangsuk kraikitrat
Bunyarit Wangngon


This research presents a method for detecting permanent magnet damage anomalies in a brushless direct current motor (BLDC Motor) by applying the adaptive neuro-fuzzy interface system (ANFIS). The input data for ANFIS has 4 lead inputs. Derived from the magtitude and position of the third order harmonics of the stator current and back electromotive force (Back-EMF) from the BLDC Motor. The ANFIS construction is ideal for detecting any malfunctions. It is the combined structure of the fuzzy logic system (FLS) and artificial neural networks (ANN) methods. In the FLS part, the membership function is used as the triangular and choose the principle of function approximation as sugeno fuzzy model and in ANN choose feed-forward network, there is transfer function at hidden layer and output layer is tan-sigmoid transfer function (tansig) and linear transfer function (purelin) respectively and have a learning style back-propagation learning from the test, it was found that the learning error of ANFIS was 3.62E-03 and the accuracy in the detection of anomalies was 98.81%.


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kraikitrat, K., & Wangngon, B. (2021). Demagnetization Fault Detection on BLDC by Adaptive Neuro Fuzzy Inference System. Naresuan University Engineering Journal, 16(2), 24–31.
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