用热成像结合自监督学习，实现电机故障高精度快速检测
Hybrid CNN-BYOL Approach for Fault Detection in Induction Motors Using Thermal Images
- 将BYOL自监督学习与CNN融合，提升热图像分类性能
- 新模型BYOL-IMNet测试准确率达99.89%，单图推理仅5.7毫秒
- 适合工业在线监测，兼顾高精度与实时性
异步电机在工业和日常生活中不可或缺，但易发生故障导致过热、能耗增加及停机。早期故障检测对保护电机、延长寿命至关重要。本文提出一种混合方法，将BYOL与CNN结合，用于异步电机热图像的故障分类。数据集包含正常运行、过载及各类故障状态。实验采用多种深度学习模型（如ResNet-50、DenseNet-121、EfficientNetB0等）进行BYOL训练，并提出一种新型轻量高效CNN模型BYOL-IMNet，由四个专为热图像故障分类设计的模块构成。结果表明，所提BYOL-IMNet在测试中达到99.89%准确率，单张图像推理时间仅5.7毫秒，优于现有先进模型。该研究展示了CNN-BYOL混合方法在提升电机故障检测精度方面的潜力，为工业场景下的在线监测提供了可靠方案。
原文摘要 · Abstract (English)
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.
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