Prognostics and health management (PHM) is essential for industrial operation and maintenance, focusing on predicting, diagnosing, and managing the health status of industrial systems. The emergence of the ChatGPT-Like large-scale language model (LLM) has begun to lead a new round of innovation in the AI field. It has extensively promoted the level of intelligence in various fields. Therefore, it is also expected further to change the application paradigm in industrial PHM and promote PHM to become intelligent. Although ChatGPT-Like LLMs have rich knowledge reserves and powerful language understanding and generation capabilities, they lack domain-specific expertise, significantly limiting their practicability in PHM applications. To this end, this study explores the ChatGPT-Like LLM empowered by the local knowledge base (LKB) in industrial PHM to solve the above limitations. In addition, we introduce the method and steps of combining the LKB with LLMs, including LKB preparation, LKB vectorization, prompt engineering, etc. Experimental analysis of real cases shows that combining the LKB with ChatGPT-Like LLM can significantly improve its performance and make ChatGPT-Like LLMs more accurate, relevant, and able to provide more insightful information. This can promote the development of ChatGPT-Like LLMs in industrial PHM and promote their efficiency and quality.

翻译：预测与健康管理（PHM）对于工业运维至关重要，其核心在于预测、诊断和管理工业系统的健康状态。ChatGPT类大规模语言模型（LLM）的出现已开始引领人工智能领域的新一轮创新，广泛提升了各领域的智能化水平。因此，该技术亦有望进一步改变工业PHM的应用范式，推动PHM向智能化方向发展。尽管ChatGPT类LLM具备丰富的知识储备和强大的语言理解与生成能力，但其缺乏领域专业知识，这在很大程度上限制了其在PHM应用中的实用性。为此，本研究探索了在工业PHM中利用本地知识库（LKB）增强ChatGPT类LLM的方法，以解决上述局限。此外，我们介绍了LKB与LLM结合的方法与步骤，包括LKB准备、LKB向量化、提示工程等。实际案例的实验分析表明，将LKB与ChatGPT类LLM结合能够显著提升其性能，使ChatGPT类LLM的输出更为精准、相关，并能提供更具洞察力的信息。这将促进ChatGPT类LLM在工业PHM中的发展，提升其应用效率与质量。