Graph isomorphism wavelet convolutional networks for small-sample fault diagnosis of rotating machinery using multi-sensor information fusion

Abstract

Multi-sensor fault monitoring of large rotating machinery inevitably encounters the problem of limited learning samples, complicating the establishment of consistent representations between monitoring data and fault attributes. To tackle this issue, a graph isomorphism wavelet convolutional network (GIWCN) is proposed for small-sample fault diagnosis with multi-sensor data fusion. GIWCN incorporates the Weisfeiler-Lehman (WL) algorithm with the interpretable node feature propagation mechanism of the graph wavelet transform (GWT) which associates multi-sensor information and discriminative structural characteristics to achieve injective isomorphic feature mapping in the spectral graph wavelet domain. To exploit the consistency of fault attributes among similar samples, isomorphic graph samples are constructed with a global topological structure under the same health states. Subsequently, graph isomorphism wavelet convolutional layer (GIWConv) is designed by embedding Multi-Layer Perceptrons (MLPs) within the GWT, thus mapping the isomorphic graphs into the same state space while ensuring the locality and sparsity of graph convolutions. Additionally, an adaptive thresholding denoising (ATD) module is integrated into the GIWConv layer to further enhance the stability of feature mapping for small samples. Finally, the isomorphic discriminative capability of GIWCN is validated on two challenging rotating machinery fault datasets, with small-sample proportions ranging from 20% to 3%. Compared to five state-of-the-art models, experimental results show that GIWCN achieves the highest diagnostic accuracy.