On optimum recognition error and reject tradeoff
IEEE Transactions on Information Theory
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
FDI based on pattern recognition using Kalman prediction: Application to an induction machine
Engineering Applications of Artificial Intelligence
Bearing Diagnosis Using Time-Domain Features and Decision Tree
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Novelty detection algorithm for data streams multi-class problems
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires
Computers and Electronics in Agriculture
Engineering Applications of Artificial Intelligence
Global geometric similarity scheme for feature selection in fault diagnosis
Expert Systems with Applications: An International Journal
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This paper deals with the diagnosis of induction motors by pattern recognition methods. The objective is to use existing theories to improve the diagnosis procedures in electrical engineering. First of all, a single signature is determined to monitor several different operating modes. For this purpose, features are extracted from the combination of the stator currents and voltages. Then, the sequential backward algorithm is applied in order to select the most relevant features. The classification is performed by the k-nearest neighbors rule with reject options. The methodology is applied on a 5.5kW motor in normal conditions, then with stator and rotor faults. The experimental results prove the efficiency of pattern recognition methods in condition monitoring of electrical machines.