Meta-Adaptive Graph Convolutional Networks With Few Samples for the Fault Diagnosis of Rotating Machinery

Rotating machinery is an important component of modern electromechanical systems and its failure can result in significant economic losses. However, existing deep learning methods only consider the features within each sample, not the neighborhood relationships among samples; this results in poor performance when few labeled samples are available. To overcome this problem, we developed a meta-adaptive graph convolutional network (MAGCNet) to uncover the neighborhood relationships among samples and construct better features for the fault diagnosis of rotating machines when labeled samples are scarce. The wavelet-packet coefficient matrices of raw vibration data are extracted and defined as node features in a graph. To enhance the correlation properties of the few samples, an adjacency matrix is constructed by measuring the Euclidean distance between time- and frequency-domain characteristics and adding prior knowledge. The graph is divided into a series of subgraphs that are trained to optimize the initialization parameters of the adaptive graph convolution layers. The effectiveness of the proposed method was verified using datasets from the drivetrain diagnostics simulator (DDS) test rig and wind-turbine gearboxes.