Cross-domain Intelligent Fault Diagnosis Using Transferable Bilinear Neural Network

Abstract

The effectiveness of conventional deep learning-based intelligent fault diagnosis models depends on the training data and testing data following the same probability distribution. But the discrepancy in cross-domain distributions is inherent because of changes in external and internal conditions, resulting in a decline in diagnosis performance. Recently, transfer learning is employed to induce an adaptive diagnosis network in the scenario of distribution discrepancies. However, little attention has been paid to fully consider the cross-layer interaction and feature transferability for traditional transfer learning-based diagnosis networks. To overcome these problems, this paper presents a novel transferable bilinear neural network for cross-domain diagnosis. First, the bilinear map between bi-layer features is used to implement a novel information fusion and significantly improves the feature representation capability. It also realizes the embedding of bi-layer joint distributions into the reproducing kernel Hilbert space. Based on the embedding and feature transferability analysis, a reliable adaptive framework is designed to enable effective cross-domain transfer learning. The effectiveness of the proposed approach is validated using experiments with various transfer scenarios.