Deep learning-based fault detection approach for squirrel cage induction motors (SCIMs) fault detection can provide a reliable solution to the industries. This paper encapsulates the idea of transfer learning-based knowledge transfer approach and deep convolutional neural network (dCNN) to develop a novel fault detection framework for multiple and simultaneous fault detection in SCIM. In comparison with the existing techniques, transfer learning-based deep CNN (TL-dCNN) method facilitates faster training and higher accuracy. The current signals acquired with the help of hall sensors and converted to an image for input to the TL-dCNN model. This approach provides autonomous learning of features and decision-making with minimum human intervention. The developed method is also compared to the existing state-of-the-art techniques, and it outperforms them and has an accuracy of 99.40%. The dataset for the TL-dCNN model is generated from the experimental setup and programming is done in python with the help of Keras and TensorFlow packages.

基于深度学习的鼠笼式感应电机 (SCIM) 故障检测方法可以为行业提供可靠的解决方案。本文封装了基于迁移学习的知识迁移方法和深度卷积神经网络 (dCNN) 的思想，以开发用于 SCIM 中多个和同时故障检测的新型故障检测框架。与现有技术相比，基于迁移学习的深度 CNN（TL-dCNN）方法有利于更快的训练和更高的准确率。在霍尔传感器的帮助下获取的当前信号并转换为图像以输入到 TL-dCNN 模型。这种方法以最少的人工干预提供了对特征和决策的自主学习。开发的方法还与现有的最先进技术进行了比较，它的表现优于他们，准确率为 99.40%。TL-dCNN 模型的数据集是从实验设置中生成的，编程是在 Keras 和 TensorFlow 包的帮助下在 python 中完成的。