A Machine-Learning-Based Fault Diagnosis Method With Adaptive Secondary Sampling for Multiphase Drive Systems
Zicheng Liu, Lanlan Fang, Dong Jiang, Ronghai Qu
Abstract
Dueto various kinds of stator phase arrangements, existing fault diagnosis (FD) methods cannot be applied to different types of multiphase machines. Spurred by the era of big data and artificial intelligence, an improved machine-learning-based FD method with adaptive secondary sampling filtering is proposed for the multiphase drive systems. Experimental results of the proposed method on both five-phase and six-phase motor drive platforms validate its satisfying generalization capability as well as high accuracy and strong robustness.
Topics & Concepts
Robustness (evolution)StatorComputer scienceControl engineeringArtificial intelligenceSampling (signal processing)Machine learningAdaptive samplingFault (geology)Ensemble learningEngineeringControl theory (sociology)MathematicsComputer visionMechanical engineeringControl (management)ChemistryFilter (signal processing)GeologyBiochemistryStatisticsMonte Carlo methodSeismologyGeneMachine Fault Diagnosis TechniquesPower System Reliability and MaintenanceAdvanced Algorithms and Applications