A data-driven approach for multiclass fault diagnosis of drive fed induction motor (IM) using stator current at steady-state condition is a complex pattern classification problem. The applied DWT-IDWT algorithm in this work is reinforced by a novel selection criterion for mother wavelet application and justifies the originality of the work. This investigation has exploited the built-in feature selection process of Random Forest (RF) classifier to resolve the most challenging issues in this area, including bearing and stator fault detection. RF has shown an outstanding performance without application of any feature selection technique because of its distributive feature model. The robustness of the results backed by the experimental verification shows an encouraging future of RF as a classifier in the area of intelligent fault diagnosis of IM.

中文翻译：

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使用随机森林算法对 VFD 馈电感应电机进行体面故障分类

在稳态条件下使用定子电流对驱动馈电感应电机 (IM) 进行多类故障诊断的数据驱动方法是一个复杂的模式分类问题。在这项工作中应用的 DWT-IDWT 算法通过一种新的母小波应用选择标准得到加强，并证明了工作的独创性。本研究利用随机森林 (RF) 分类器的内置特征选择过程来解决该领域最具挑战性的问题，包括轴承和定子故障检测。RF由于其分布式特征模型，在没有应用任何特征选择技术的情况下表现出出色的性能。由实验验证支持的结果的稳健性表明，RF 作为分类器在 IM 智能故障诊断领域的前景令人鼓舞。