School of Electronics Engineering, Kyungpook National University, Daegu 41566, Republic of Korea.
Sensors (Basel). 2023 Feb 26;23(5):2585. doi: 10.3390/s23052585.
Induction motors are robust and cost effective; thus, they are commonly used as power sources in various industrial applications. However, due to the characteristics of induction motors, industrial processes can stop when motor failures occur. Thus, research is required to realize the quick and accurate diagnosis of faults in induction motors. In this study, we constructed an induction motor simulator with normal, rotor failure, and bearing failure states. Using this simulator, 1240 vibration datasets comprising 1024 data samples were obtained for each state. Then, failure diagnosis was performed on the acquired data using support vector machine, multilayer neural network, convolutional neural network, gradient boosting machine, and XGBoost machine learning models. The diagnostic accuracies and calculation speeds of these models were verified via stratified K-fold cross validation. In addition, a graphical user interface was designed and implemented for the proposed fault diagnosis technique. The experimental results demonstrate that the proposed fault diagnosis technique is suitable for diagnosing faults in induction motors.
感应电动机坚固耐用且价格经济实惠；因此，它们通常被用作各种工业应用中的电源。然而，由于感应电动机的特性，当电机发生故障时，工业过程可能会停止。因此，需要进行研究以实现对感应电动机故障的快速准确诊断。在本研究中，我们构建了一个具有正常、转子故障和轴承故障状态的感应电动机模拟器。使用该模拟器，我们为每个状态获得了 1240 个振动数据集，每个数据集包含 1024 个数据样本。然后，使用支持向量机、多层神经网络、卷积神经网络、梯度提升机和 XGBoost 机器学习模型对采集到的数据进行故障诊断。通过分层 K 折交叉验证验证了这些模型的诊断准确性和计算速度。此外，还为提出的故障诊断技术设计并实现了一个图形用户界面。实验结果表明，所提出的故障诊断技术适用于感应电动机的故障诊断。
