PHM-Vibench

The Prognostics and Health Management (PHM) field faces significant challenges due to fragmented benchmarks, inconsistent evaluation protocols, and limited accessibility to comprehensive frameworks, particularly in the era of large-scale data and foundation models. To address these critical limitations, we introduce PHM-Vibench, a unified, extensible, and modular benchmarking platform for vibration-based PHM research. PHM-Vibench features a novel architecture that decouples the PHM pipeline into distinct data, model, task, and trainer factories, enabling flexible instantiation and customization of specific PHM workflows. The platform integrates comprehensive 20+ datasets with standardized protocols. It supports diverse PHM tasks including fault diagnosis, remaining useful life prediction, and anomaly detection. The framework addresses complex scenarios such as domain generalization, cross-system transfer, few-shot learning. Grounded in the Unified PHM Problem (UPHMP) framework with seven fundamental spaces: domain knowledge space (P), data space (D), task space (T), model space (M), loss function space (L), protocol space (Π), and evaluation metric space (E), PHM-Vibench enables systematic problem formalization and reproducible experimentation. The platform accommodates both traditional machine learning models and foundation models, with extensive experimental validation demonstrating superior cross-domain performance. PHMVibench addresses the standardization challenges in PHM research and provides a comprehensive solution for benchmarking and advancing the field. The platform is openly available at https://github.com/PHMbench/PHM-Vibench.