Fault Diagnosis of Gearbox based on Convolutional Neural Network and Infrared Thermal Imagining

Diagnosis of gearbox is crucial to prevent catastrophic failure and reduce financial losses. In this study, we introduce a novel fault diagnosis technique using the infrared thermography (IRT). The IRT-based techniques have merits of non-contact measurement and high-scalability. Since the convolutional neural network (CNN) is proven to be powerful in image processing, a fault diagnosis strategy is designed by combining the IRT and CNN. Then, the pattern identification is achieved by using softmax regression (SR) classifier. One experimental data is used to validate the effectiveness of the proposed method. Results demonstrate that this diagnosis strategy can recognize gearbox with various oil-level faults. Furthermore, some important distinguishable areas of IRT images are marked for further focused research field.