A Virtual Domain-Driven Semi-Supervised Hyperbolic Metric Network With Domain-Class Adversarial Decoupling for Aircraft Engine Intershaft Bearings Fault diagnosis

Abstract

Aircraft engines operate under more demanding and unique environments, which require the inner components to be able to withstand extreme conditions. Intershaft bearings serve as the critical part of power transmission. Therefore, their accurate and reliable fault diagnosis is of paramount importance to ensure secure and dependable functioning of the engine. In this field, scarcity of labeled fault data owing to high collection costs is a common challenge. To address this, this article proposes a semi-supervised cross-domain diagnostic method for aircraft engine intershaft bearings, utilizing a virtual domain-driven approach to achieve high accuracy with limited labeled data. Specifically, a dynamics-based simulation model is developed to generate source domain data, reducing the dependency of deep learning models on experimental platforms and lowering platform construction costs. Additionally, a hyperbolic geometric metric learning strategy is designed to capture hierarchical features in high-dimensional data, which handles the correlation between different fault types and enhancing classification accuracy. Furthermore, a domain-class adversarial decoupling mechanism is developed to mitigate the domain bias, enabling the precise representation of fault modes and maximizing the utility of unlabeled virtual domain data. Using datasets from both real-world aircraft engine scenarios and public resource experiments validate the proposed method, illustrating its superior performance compared to state-of-the-art techniques on public domain benchmark datasets.