Episodic Training and Feature Orthogonality-Driven Domain Generalization for Rotating Machinery Fault Diagnosis Under Unseen Working Conditions

Episodic Training and Feature Orthogonality-Driven Domain Generalization for Rotating Machinery Fault Diagnosis Under Unseen Working Conditions

In recent years, domain generalization-based fault diagnosis (DGFD) methods have shown significant potential in rotating machinery fault diagnosis in unseen target domains. However, these methods focus on learning domain-invariant representations via feature distribution adaptation. The generalizati...

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Bibliographic Details
Main Authors: Yixiao Liao, Songbin Zhou, Yisen Liu, Kunkun Pang, Jing Li, Chang Li, Lulu Zhao
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Machines
Subjects:
fault diagnosis
rotating machinery
domain generalization
episodic training
feature orthogonality
Online Access:https://www.mdpi.com/2075-1702/13/7/563
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https://www.mdpi.com/2075-1702/13/7/563

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