Efficient fault diagnosis of electrical and mechanical anomalies in induction motors (IMs) is challenging but necessary to ensure safety and economical operation in industries. Research has shown that bearing faults are the most frequently occurring faults in IMs. The vibration signals carry rich information about bearing health conditions and are commonly utilized for fault diagnosis in bearings. However, collecting these signals is expensive and sometimes impractical because it requires the use of external sensors. The external sensors demand enough space and are difficult to install in inaccessible sites. To overcome these disadvantages, motor current signal-based bearing fault diagnosis methods offer an attractive solution. As such, this paper proposes a hybrid motor-current data-driven approach that utilizes statistical features, genetic algorithm (GA) and machine learning models for bearing fault diagnosis. First, the statistical features are extracted from the motor current signals. Second, the GA is utilized to reduce the number of features and select the most important ones from the feature database. Finally, three different classification algorithms namely KNN, decision tree, and random forest, are trained and tested using these features in order to evaluate the bearing faults. This combination of techniques increases the accuracy and reduces the computational complexity. The experimental results show that the three classifiers achieve an accuracy of more than 97%. In addition, the evaluation parameters such as precision, F1-score, sensitivity, and specificity show better performance. Finally, to validate the efficiency of the proposed model, it is compared with some recently adopted techniques. The comparison results demonstrate that the suggested technique is promising for diagnosis of IM bearing faults.

中文翻译：

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基于遗传算法和机器学习分类器的异步电动机轴承故障诊断

对感应电动机（IM）的电气和机械异常进行有效的故障诊断具有挑战性，但对于确保工业中的安全性和经济性运行而言，这是必需的。研究表明，轴承故障是IM中最常见的故障。振动信号携带有关轴承健康状况的丰富信息，通常用于轴承故障诊断。但是，收集这些信号是昂贵的，有时是不切实际的，因为它需要使用外部传感器。外部传感器需要足够的空间，并且难以安装在无法访问的位置。为了克服这些缺点，基于电动机电流信号的轴承故障诊断方法提供了一种有吸引力的解决方案。因此，本文提出了一种利用统计特征的混合电动机电流数据驱动方法，遗传算法（GA）和机器学习模型进行轴承故障诊断。首先，从电动机电流信号中提取统计特征。其次，利用遗传算法减少特征数量并从特征数据库中选择最重要的特征。最后，使用这些功能训练和测试了三种不同的分类算法，即KNN，决策树和随机森林，以评估轴承故障。这种技术组合提高了准确性并降低了计算复杂度。实验结果表明，三个分类器的准确率均超过97％。此外，评估参数（如精度，F1得分，灵敏度和特异性）表现出更好的性能。最后，要验证所提出模型的效率，它与最近采用的一些技术进行了比较。比较结果表明，该技术有望用于IM轴承故障的诊断。