Abstract
Induction motors (IMs) are indispensable in industrial and daily life, but they are susceptible to various faults that can lead to overheating, wasted energy consumption, and service failure. Early detection of faults is essential to protect the motor and prolong its lifespan. This paper presents a hybrid method that integrates BYOL with CNNs for classifying thermal images of induction motors for fault detection. The thermal dataset used in this work includes different operating states of the motor, such as normal operation, overload, and faults. We employed multiple deep learning (DL) models for the BYOL technique, ranging from popular architectures such as ResNet-50, DenseNet-121, DenseNet-169, EfficientNetB0, VGG16, and MobileNetV2. Additionally, we introduced a new high-performance yet lightweight CNN model named BYOL-IMNet, which comprises four custom-designed blocks tailored for fault classification in thermal images. Our experimental results demonstrate that the proposed BYOL-IMNet achieves 99.89\% test accuracy and an inference time of 5.7 ms per image, outperforming state-of-the-art models. This study highlights the promising performance of the CNN-BYOL hybrid method in enhancing accuracy for detecting faults in induction motors, offering a robust methodology for online monitoring in industrial settings.
Abstract (translated)
感应电机（IMs）在工业和日常生活中不可或缺，但它们容易出现各种可能导致过热、能源浪费和服务故障的故障。早期检测这些故障对于保护电机并延长其寿命至关重要。本文提出了一种结合BYOL与CNN的方法，用于分类感应电机的热图像以进行故障检测。本研究使用的热数据集包含了电机的不同运行状态，包括正常操作、超载和各种故障状态。 我们采用了多种深度学习（DL）模型来实施BYOL技术，其中包括流行的架构如ResNet-50、DenseNet-121、DenseNet-169、EfficientNetB0、VGG16及MobileNetV2。此外，还提出了一种新的高性能且轻量级的CNN模型——BYOL-IMNet，该模型由四个专为热图像故障分类设计的自定义块组成。 实验结果表明，所提出的BYOL-IMNet模型在测试中达到了99.89%的准确率，并具有每张图像仅需5.7毫秒推理时间的优点，优于现有的最佳模型。本研究展示了CNN-BYOL混合方法在提高感应电机故障检测准确性方面的有前景的表现，为工业环境中的在线监测提供了稳健的方法论。
URL
https://arxiv.org/abs/2510.07692
