Intelligent acoustic-based fault diagnosis of roller bearings using a deep graph convolutional network

Roller bearings form key components in many machines and, as such, their health status can directly influence the operation of the entire machine. Acoustic signals collected from roller bearings contain information on their health status. Hence, acoustic-based fault diagnosis techniques can provide novel solutions as condition monitoring tools for roller bearings. Traditionally, acoustic fault diagnosis methods have been based on conventional signal processing methods in which prior expert knowledge has been required in order to extract and interpret the health information contained within the collected acoustic signals. As an alternative, deep learning methods can be used to obtain heath information from the collected signals by constructing ‘end-to-end’ models that do not rely on prior knowledge. These approaches have been successfully applied in the condition monitoring of industrial machinery. However, conventional deep learning methods can only learn features from the vertices of input data and thereby ignore the information contained in the relationships (edges) between vertices. In this paper, which combines graph convolution operators, graph coarsening methods, and graph pooling operations; a deep graph convolutional network (DGCN) based on graph theory is applied to deliver acoustic-based fault diagnosis of roller bearings. In the proposed method, the collected acoustic signals are first transformed into graphs with geometric structures. The edge weights represent the similarity between connected vertices, which enriches the input information and hence improves the classification accuracy of the deep learning methods applied. To verify the effectiveness of the proposed system, experiments with roller bearings of varying condition were carried out in the laboratory. The experimental results demonstrate that the DGCN method can be used to detect different kinds and severities of faults in roller bearings by learning from the constructed graphs. The results have been compared to those obtained using other, conventional, deep learning methods applied to the same datasets. These comparative tests demonstrate improved classification accuracy when using the DGCN method.