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Fault diagnosis is very important in ensuring safe and reliable operation in manufacturing systems. This paper presents an adaptive artificial immune classification approach for diagnosis of induction motor faults. The proposed algorithm uses memory cells tuned using the magnitude of the standard deviation obtained with average affinity variation in each generation. The algorithm consists of three...
This paper presents an artificial immune system based classification rules generation for fault diagnosis of induction motors. To implement the proposed method effectively, a feature extraction and fuzzificiation processes are used for choosing fault-related attributes from motor current signals. The idea behind the method is mainly based on both concepts of data mining and artificial immune system...
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