arXiv:2605.28345v1 Announce Type: cross Abstract: Progress in Prognostics and Health Management (PHM) is hindered by the lack of standardized and reusable evaluation practices across tasks, datasets, and application domains. Reported results are often difficult to reproduce and compare, as key protocol choices, such as data splits, preprocessing, label alignment, temporal windowing, and metrics, are often implicit or implemented ad hoc. We introduce \picid, a modular evaluation infrastructure that formalizes the PHM evaluation pipeline as an explicit, executable, and reproducible protocol. Thr
The rapid advancement and deployment of AI in diverse applications necessitate more rigorous, standardized, and reproducible evaluation methodologies to ensure reliability and trust.
This development addresses a critical bottleneck in AI progress, facilitating more reliable comparisons of PHM models and accelerating the transition of research into robust real-world applications.
The introduction of a modular infrastructure standardizes PHM evaluation, moving away from ad hoc practices, which will improve the quality and trustworthiness of AI-driven prognostic systems.
- · AI researchers
- · Industrial operators
- · Regulators
- · Predictive maintenance sector
- · Companies relying on opaque or unreproducible AI evaluations
- · Ad hoc evaluation methodologies
Improved reproducibility and comparability of AI models in prognostics and health management.
Faster development cycles and more reliable deployment of AI solutions across critical infrastructure and manufacturing.
Enhanced trust in AI systems, potentially accelerating their integration into safety-critical domains with significant economic and societal impact.
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