CNN based Gearbox Fault Diagnosis and Interpretation of Learning Features

Machine learning based fault diagnosis schemes have been intensively proposed to deal with faults diagnosis of rotating machineries such as gearboxes, bearings, and electric motors. However, most of the machine learning algorithms used in fault diagnosis are pattern recognition tools, which can classify given data into two or more classes. The underlined physical phenomena in fault diagnosis are not directly interpretable in machine learning schemes, thus it is usually called black/gray box models. In this study, convolutional neural networks (CNN) machine learning algorithm is proposed to classify gearbox faults, and the learning features of the CNN filters are visualized to understand the physical fault diagnosis phenomena. Within the framework, a detailed explanation is given based on domain knowledge of gearbox fault diagnosis, and the physical phenomena are explained by fault characteristic frequencies, allowing for observing the relationship between the characteristic frequencies and the CNN feature learning filters. The proposed algorithm is validated using an in-house experimental setup.