A universal fault diagnosis framework for marine machinery based on domain adaptation

Abstract

In the digital and automated era, the significance of machine health monitoring has been amplified, especially for marine machinery, which plays a pivotal role in the sustainable advancement of modern industries. Two salient challenges currently hampering fault diagnostics are spotlighted: firstly, the variability in mechanical operating conditions gives rise to inconsistent data distributions, thereby prompting domain shift. Secondly, unobserved in training datasets, testing scenarios may unveil unpredictable, unknown fault modes, culminating in class discrepancies. To effectively tackle these intertwined challenges, we introduce the Two-Stage Universal Domain Adaptation (TSUDA) — a novel fault diagnosis methodology. TSUDA adeptly differentiates new fault types from labeled known counterparts by leveraging a composite transferability metric. Furthermore, it deploys a parameter-adaptive unsupervised algorithm to discern the count of emerging fault types. Rigorous experiments on datasets from marine diesel engines, emblematic of marine machinery, demonstrated TSUDA's superiority over conventional domain adaptation techniques in diverse diagnostic challenges, registering accuracy rates surpassing 90%. While the effectiveness of TSUDA is authenticated within marine machinery contexts in this investigation, its architecture suggests potential applicability in a broader spectrum of mechanical fault diagnostic scenarios.