Data fusion for multiple mechanical fault diagnosis in induction motors at variable operating conditions
作者： Jose D. Martinez-Morales , E. Palacios , D. U. Campos-Delgado
DOI: 10.1109/ICEEE.2010.5608632
关键词:
摘要: In this paper, data fusion based on multi-class support vector machines (SVM) is presented to detect and isolate three mechanical faults in induction motors. First, we construct the feature by using signatures created from frequency-domain characteristics. These are obtained vibration line currents measurements. Then, used feed SVM's classify different motor conditions (normal, misalignment, unbalanced bearing fault). Different experiments a phase were performed under variable operational (motor speeds load torque scenarios) order acquire training validation data. The identified optimal parameters of reported. studied with two types kernel functions, radial basis polynomial functions. Data acquisition, extraction computation implemented LabView programming language. experimental results show effectiveness proposed approach diagnosing at conditions. these tests, worst-case accuracy method was 97.1%.
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