PHM-Bench: A Domain-Specific Benchmarking Framework for Systematic Evaluation of Large Models in Prognostics and Health Management

2025-08-04

AI Summary

Current large language model (LLM) evaluation frameworks in the field of Prognostics and Health Management (PHM) suffer from structural fragmentation, unidimensional assessment, and coarse-grained metrics, hindering their engineering deployment. To address this, we propose PHM-Bench—the first three-dimensional, PHM-specific evaluation framework—structured along three orthogonal dimensions: foundational capabilities, core PHM tasks, and full-lifecycle applicability. We introduce three fine-grained, task-relevant metrics: knowledge comprehension, algorithm generation, and task optimization. PHM-Bench integrates curated industrial case studies and public PHM datasets to enable multi-scenario empirical evaluation. It is the first framework to provide unified, comparable, and interpretable assessment across canonical PHM tasks—including condition monitoring, fault diagnosis, and remaining useful life prediction. By establishing a standardized benchmark and methodological foundation, PHM-Bench bridges the gap between general-purpose LLMs and domain-specialized PHM models.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

With the rapid advancement of generative artificial intelligence, large language models (LLMs) are increasingly adopted in industrial domains, offering new opportunities for Prognostics and Health Management (PHM). These models help address challenges such as high development costs, long deployment cycles, and limited generalizability. However, despite the growing synergy between PHM and LLMs, existing evaluation methodologies often fall short in structural completeness, dimensional comprehensiveness, and evaluation granularity. This hampers the in-depth integration of LLMs into the PHM domain. To address these limitations, this study proposes PHM-Bench, a novel three-dimensional evaluation framework for PHM-oriented large models. Grounded in the triadic structure of fundamental capability, core task, and entire lifecycle, PHM-Bench is tailored to the unique demands of PHM system engineering. It defines multi-level evaluation metrics spanning knowledge comprehension, algorithmic generation, and task optimization. These metrics align with typical PHM tasks, including condition monitoring, fault diagnosis, RUL prediction, and maintenance decision-making. Utilizing both curated case sets and publicly available industrial datasets, our study enables multi-dimensional evaluation of general-purpose and domain-specific models across diverse PHM tasks. PHM-Bench establishes a methodological foundation for large-scale assessment of LLMs in PHM and offers a critical benchmark to guide the transition from general-purpose to PHM-specialized models.

Problem

• Evaluate LLMs in PHM lacking structural completeness and granularity
• Develop PHM-Bench for multi-dimensional model assessment in PHM tasks
• Bridge gap from general-purpose to PHM-specialized LLM evaluation

Innovation

• Three-dimensional evaluation framework for PHM
• Multi-level metrics for PHM tasks
• Case sets and datasets for model assessment