Huailiang Zheng

TL;DR: This paper for the first time summarizes the state-of-art cross-domain fault diagnosis research works from three different viewpoints: research motivations, cross- domain strategies, and application objects and provides readers a framework for better understanding and identifying the research status, challenges and future directions of cross- domains fault diagnosis.

TL;DR: A novel intelligent fault identification method based on multiple source domains that describes the discriminant structure of each source domain as a point of Grassmann manifold using local Fisher discriminant analysis to learn effective discriminant directions from multimodal fault data.

TL;DR: A fault diagnosis scheme combined of hierarchical symbolic analysis (HSA) and convolutional neural network (CNN) is proposed, which achieves laborsaving and timesaving preliminary feature extraction and accomplishes automatically feature learning with simplified network architecture.

TL;DR: The results show that based on the simulation data and parameter transfer strategies in CNN, the proposed method can learn more transferable features and reduce the feature distribution discrepancy, contributing to enhancing the fault identification performance significantly.

TL;DR: This article presents a diagnosis scheme for rolling bearing under a challenging domain generalization scenario, in which more potential discrepancies among multiple source domains are eliminated and only normal samples of the target domain are available during the training stage.