From Inconsistency to Unity: Benchmarking Deep Learning-Based Unsupervised Domain Adaptation for RUL

Data-driven Remaining Useful Life (RUL) estimation is critical for various industries, yet scarce labeled data poses a significant challenge. Unsupervised Domain Adaptation (UDA) combined with deep learning has emerged as a promising solution by leveraging unlabeled data. However, recent work on deep UDA exhibits notable inconsistencies, including variations in backbone networks and data handling. Such inconsistencies hinder fair comparisons, obscuring if actual advances were made. To address this, we propose CRULE, a comprehensive benchmarking framework that standardizes deep UDA evaluation. CRULE incorporates a unified 1-D CNN backbone architecture, a standardized training scheme with comprehensive hyperparameter tuning, consistent evaluation protocols (both inductive and transductive), and unified performance metrics (RMSE and Score). This ensures fair and reliable comparisons across different UDA methods. Through experiments on three popular RUL datasets, we found, that only one of the evaluated approaches achieves statistically significant improvements over no adaptation. This suggests that deep UDA approaches proposed for RUL estimation may be less reliable under fair evaluation schemes. To catalyze genuine advancements in the field, we open-source CRULE, empowering the research community to develop and consistently benchmark UDA approaches. CRULE is accessible at https://anonymous.4open.science/r/crule-55D1.