circuit analysis computing; fault diagnosis; fault tolerance; feature extraction; feature selection; genetic algorithms; neural nets; reluctance motors; signal classification; stators; time-frequency analysis; ANN intelligent approach; GA; TFR; artificial neural network; automatic fault diagnosis; classification method; classified SRM open-circuit faults; fault tolerance; genetic approach; nonlinear classifier based ANN; optimum feature extraction technique; optimum feature selection techniques; reluctance motors; smoothing time-frequency representation method; stator winding related fault detection; switched reluctance machine; time-frequency ambiguity plane; torque time signals; winding open-circuits; Circuit faults; Coils; Fault diagnosis; Feature extraction; Reluctance motors; Torque; Training; fault diagnosis; genetic algorithm; machine-learning; neural networks; switched reluctance machine; time-frequency representation;
机译:基于特征提取,特征选择和分类算法的感应电动机故障诊断系统
机译:基于特征提取,特征选择和分类算法的感应电动机故障诊断系统
机译:基于k-NN和最优特征选择的感应电动机故障诊断
机译:磁阻电动机自动故障诊断的最佳特征提取与选择
机译:感应电机的自动传感器故障诊断和控制器重新配置可驱动传感器的容错能力。
机译:基于特征融合的轴承故障特征提取与故障诊断方法
机译:使用特征提取和自动分区模糊聚类诊断变压器故障