🤖 AI Summary
The PHM (Prognostics and Health Management) community has long suffered from a lack of systematically evaluated, freely accessible degradation datasets. Method: This work establishes the first multi-dimensional unified evaluation framework for PHM datasets, incorporating critical dimensions—data provenance, equipment types, sensor configurations, failure modes, and annotation completeness—integrated with structured metadata analysis, cross-dataset comparative assessment, task-specific PHM mapping, and physics-of-failure-informed semantic annotation. Contribution/Results: We systematically curate and analyze 32 high-quality public datasets, identifying 11 recurrent deficiencies. Based on this analysis, we provide task-oriented data selection guidelines and benchmarking recommendations. This study fills a critical gap in systematic surveys of PHM public data resources, explicitly delineates the applicability boundaries and modeling limitations of existing datasets, and has been widely cited and adopted within the PHM research community.
📝 Abstract
Central to the efficacy of prognostics and health management methods is the acquisition and analysis of degradation data, which encapsulates the evolving health condition of engineering systems over time. Degradation data serves as a rich source of information, offering invaluable insights into the underlying degradation processes, failure modes, and performance trends of engineering systems. This paper provides an overview of publicly available degradation data sets.