PHM-Vibench: A unified, extensible, and reproducible vibration benchmarking framework for prognostics and health management

Abstract

 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.