The bearings are the crucial components of rotating machines in an industrial firm. Unplanned failure of these components not only increases the downtime, but also leads to production loss. This paper presents a non-invasive thermal image-based method for bearing fault diagnosis in rotating machines. Thermal images of rolling-element bearing in six conditions have been considered, including one healthy and five faulty conditions, and then a comparison based on classification performance has been done using shallow and deep learning approaches incorporating artificial neural network (ANN) and convolutional neural network (CNN). The CNN used in this work is based on the LeNet-5 structure and has proved to be a better than the ANN. It has been concluded that infrared thermography can be used in a non-contact way to automatically identify the faults that help to detect early warnings, irrespective of speeds and hence ensures reduced system shutdowns causing by bearing failure.

轴承是工业公司中旋转机械的关键部件。这些组件的计划外故障不仅会增加停机时间，而且还会导致生产损失。本文提出了一种基于非侵入式热图像的旋转机械轴承故障诊断方法。考虑了滚动轴承的六种情况下的热图像，包括一个健康状态和五个故障状态，然后使用结合了人工神经网络（ANN）和卷积神经网络的浅层和深度学习方法，基于分类性能进行了比较。 （CNN）。这项工作中使用的CNN基于LeNet-5结构，并被证明比ANN更好。