隨著智慧製造與數位轉型發展，設備於需求規劃、選型、採購、安裝、驗收、身分檔案建立、運行、巡檢、保養、維修、更新與報廢等生命週期階段，產生設備手冊、故障資料、紀錄表、物料清單（Bill of Materials, BOM）、爆炸圖、語音及影片等多模態資料。而設備知識多分散於不同文件或系統，若僅採文件保存、人工分類或關鍵字查詢，難以支援人員快速取得、追溯與再利用。因此，本研究提出以生成式人工智慧為基礎之設備知識管理模式，並開發可於本地端運行之多代理人系統。
系統由路由代理人負責任務分派，資料處理代理人整合 PDF、Excel、文字、表格、圖片、語音與影片等多模態解析技術，將原始資料轉換為具設備型號、生命週期階段、知識類型與來源資訊之知識單元，並建立 BOM 零件與爆炸圖圖片之對應關係。知識資料分別儲存於 SQLite、Chroma 與 Neo4j，支援結構化資料管理、向量語意檢索與知識圖譜關聯查詢；知識圖譜區分為概念知識層、經驗法則知識層與圖像知識層。查詢與決策支援代理人負責問題理解與檢索路徑選擇，Reciprocal Rank Fusion（RRF）融合 Chroma 向量檢索與 Neo4j 知識圖譜檢索結果，最後由回應代理人整合檢索內容與來源資訊，並呼叫本地端 Qwen 模型產生具來源依據之回答。
本研究以螺旋攪拌機為案例，透過七項代表性問題及30題量化題庫進行系統驗證。結果顯示，混合式檢索之Hit@5為1.0000、MRR為0.9361，Faithfulness為0.8428，Source Accuracy為1.0000。綜合上述結果，本研究方法可將分散且異質之設備資料轉化為可管理、可檢索及可追溯之設備知識，支持其應用於設備知識管理與決策支援之技術可行性。

With the development of smart manufacturing, equipment generates heterogeneous and multimodal data throughout its lifecycle. However, such knowledge is often dispersed across different documents and systems, making it difficult to retrieve, trace, and reuse. Therefore, this study proposes a generative AI-based equipment knowledge management model and develops a locally deployable multi-agent system.

The system coordinates multiple agents to transform multimodal data, including PDF, Excel, images, audio, and video, into knowledge units containing equipment models, lifecycle stages, knowledge types, and source information. It also establishes relationships between bill of materials (BOM) parts and exploded-view images. After review, approved knowledge is indexed in Chroma and stored in Neo4j, while SQLite manages the structured data. Reciprocal Rank Fusion (RRF) is used to fuse the Chroma and Neo4j retrieval results, which are then provided to a locally deployed Qwen model to generate source-grounded answers.
A spiral mixer was used as the case equipment. The system was evaluated using seven representative questions and a 30-question evaluation set. Hybrid retrieval achieved a Hit@5 of 1.0000 and an MRR of 0.9361, while Faithfulness and Source Accuracy reached 0.8428 and 1.0000. The results support the technical feasibility of the proposed system for equipment knowledge management and decision support.

中國設備管理協會. (2017). 設備管理體系 要求(T/CAPE 10001—2017 ).
Alavi, M., & Leidner, D. E. (2001). Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues. MIS Quarterly, 25(1), 107–136.
Bahr, L., Wehner, C., Wewerka, J., Bittencourt, J., Schmid, U., & Daub, R. (2025). Knowledge graph enhanced retrieval-augmented generation for failure mode and effects analysis. Journal of Industrial Information Integration, 45, 100807.
Borgeaud, S., Mensch, A., Hoffmann, J., Cai, T., Rutherford, E., Millican, K., Van Den Driessche, G. B., Lespiau, J.-B., Damoc, B., Clark, A., De Las Casas, D., Guy, A., Menick, J., Ring, R., Hennigan, T., Huang, S., Maggiore, L., Jones, C., Cassirer, A.,…Sifre, L. (2022). Improving language models by retrieving from trillions of tokens. In Proceedings of the 39th International Conference on Machine Learning (Vol. 162, pp. 2206–2240). PMLR.
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C.,…Amodei, D. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems,
Chen, L.-C., Pardeshi, M. S., Liao, Y.-X., & Pai, K.-C. (2025). Application of retrieval-augmented generation for interactive industrial knowledge management via a large language model. Computer Standards & Interfaces, 94.
Cheng, Y. J., & Chen, S. L. (2026). An Event-Driven Dual-Mode Multilingual RAG Framework for Industrial Maintenance. IEEE Access, 14, 81494–81514.
Cormack, G. V., Clarke, C. L. A., & Buettcher, S. (2009). Reciprocal rank fusion outperforms condorcet and individual rank learning methods. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval (Vol. pp. 758–759). Association for Computing Machinery.
D'Cruze, R. S., Bengtsson, M., Funk, P., & Sohlberg, R. (2026). A Generative AI Framework for Smart Maintenance: Utilizing RAG Systems and LLMs to Assist Manufacturing Operations. In International Congress and Workshop on Industrial AI and eMaintenance 2025 (pp. 189–202). https://doi.org/10.1007/978-3-032-03725-1_13
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019, June). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In J. Burstein, C. Doran, & T. Solorio, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) Minneapolis, Minnesota.
Duval, E., Hodgins, W., Sutton, S., & Weibel, S. L. (2002). Metadata Principles and Practicalities. D-Lib Magazine, 8(4).
Elahi, M., Afolaranmi, S. O., Martinez Lastra, J. L., & Perez Garcia, J. A. (2023). A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment. Discover Artificial Intelligence, 3(1), 43.
Es, S., James, J., Espinosa Anke, L., & Schockaert, S. (2024, March). RAGAs: Automated Evaluation of Retrieval Augmented Generation. In N. Aletras & O. De Clercq, Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations St. Julians, Malta.
Fahmy, T., Minda, R., & Pak, S. (2026). The Role of AI in Enhancing Knowledge Management Processes. In AI-Driven Knowledge Management Processes, Volume 1: Strategies for the Modern Business Landscape (pp. 23–38). Emerald Publishing Limited. https://doi.org/10.1108/978-1-80592-391-620261002
Feng, S. C., Bernstein, W. Z., Hedberg, T., Jr., & Feeney, A. B. (2017). Towards Knowledge Management for Smart Manufacturing. Journal of Computing and Information Science in Engineering, 17(3).
Hogan, A., Blomqvist, E., Cochez, M., D’amato, C., Melo, G. D., Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., Ngomo, A.-C. N., Polleres, A., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., & Zimmermann, A. (2021). Knowledge Graphs. ACM Comput. Surv., 54(4), Article 71.
Izacard, G., & Grave, E. (2021). Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume,
Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S. (2022). A Survey on Knowledge Graphs: Representation, Acquisition, and Applications. IEEE Transactions on Neural Networks and Learning Systems, 33(2), 494–514.
Kamm, S., Jazdi, N., & Weyrich, M. (2021). Knowledge Discovery in Heterogeneous and Unstructured Data of Industry 4.0 Systems: Challenges and Approaches. Procedia CIRP, 104, 975–980.
Kamm, S., Veekati, S. S., Müller, T., Jazdi, N., & Weyrich, M. (2023). A survey on machine learning based analysis of heterogeneous data in industrial automation. Computers in Industry, 149, 103930.
Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-t. (2020, November). Dense Passage Retrieval for Open-Domain Question Answering. In B. Webber, T. Cohn, Y. He, & Y. Liu, Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) Online.
Kernan Freire, S., Wang, C., Foosherian, M., Wellsandt, S., Ruiz-Arenas, S., & Niforatos, E. (2024). Knowledge sharing in manufacturing using LLM-powered tools: user study and model benchmarking [Brief Research Report]. Frontiers in Artificial Intelligence, Volume 7 - 2024.
Kim, S., Jeon, H., Kim, D., Kim, M., Chae, D.-K., & Kim, J. (2025). HybridRAG: A Practical LLM-based ChatBot Framework based on Pre-Generated Q&A over Raw Unstructured Documents. arXiv preprint arXiv:2602.11156.
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large language models are zero-shot reasoners. In Proceedings of the 36th International Conference on Neural Information Processing Systems (Vol. pp. Article 1613). Curran Associates Inc.
Lecue, F. (2020). On the role of knowledge graphs in explainable AI. Semantic Web, 11(1), 41–51.
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. In Proceedings of the 34th International Conference on Neural Information Processing Systems (Vol. pp. Article 793). Curran Associates Inc.
Li, Y., Dong, Z., Ameri, F., & Zhang, J. (2026). ManuRAG: multi-modal retrieval augmented generation for manufacturing question answering. Journal of Intelligent Manufacturing.
Lin, C., & Tseng, S.-M. (2005). Bridging the implementation gaps in the knowledge management system for enhancing corporate performance. Expert Systems with Applications, 29(1), 163–173.
Love, P. E. D., Zhou, J., Matthews, J., & Luo, H. (2016). Systems information modelling: Enabling digital asset management. Advances in Engineering Software, 102, 155–165.
Mansor, M. A., Ohsato, A., & Sulaiman, S. (2012). Knowledge Management for Maintenance Activities in the Manufacturing Sector. International Journal of Automotive and Mechanical Engineering, 5, 612–621.
ManualsLib. (n.d.). SINMAG SM2-50T operation manual: Spiral mixer. Retrieved 2026/07/04 from https://www.manualslib.com/manual/4162875/Sinmag-Sm2-50t.html
Mao, T., Yang, S., & Fu, B. (2025). A Multi-Agent Framework for Multi-Source Manufacturing Knowledge Integration and Question Answering. In Companion Proceedings of the ACM on Web Conference 2025 (Vol. pp. 1687–1695). Association for Computing Machinery.
Masoudifard, A., Sorond, M., Madadi, M., Sabokrou, M., & Habibi, E. (2024). Leveraging Graph-RAG and Prompt Engineering to Enhance LLM-Based Automated Requirement Traceability and Compliance Checks. arXiv.
Microsoft. Copilot and AI agents. Microsoft. https://www.microsoft.com/en-us/microsoft-copilot/copilot-101/copilot-ai-agents
Nonaka, I., Toyama, R., & Konno, N. (2000). SECI, Ba and Leadership: a Unified Model of Dynamic Knowledge Creation. Long Range Planning, 33(1), 5–34.
Park, J. S., O'Brien, J., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023). Generative Agents: Interactive Simulacra of Human Behavior. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (Vol. pp. Article 2). Association for Computing Machinery.
Polenghi, A., Roda, I., Macchi, M., Pozzetti, A., & Panetto, H. (2022). Knowledge reuse for ontology modelling in Maintenance and Industrial Asset Management. Journal of Industrial Information Integration, 27, 100298.
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning Transferable Visual Models From Natural Language Supervision. In Proceedings of the 38th International Conference on Machine Learning (Vol. 139, pp. 8748––8763). PMLR.
Rahali, A., & Akhloufi, M. A. (2023). End-to-End Transformer-Based Models in Textual-Based NLP. AI, 4(1), 54–110.
Raiaan, M. A. K., Mukta, M. S. H., Fatema, K., Fahad, N. M., Sakib, S., Mim, M. M. J., Ahmad, J., Ali, M. E., & Azam, S. (2024). A Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges. IEEE Access, 12, 26839–26874.
Rüth, M., Sullmann, M., & Hussain, A. (2026). Design Principles for Integrating Generative Artificial Intelligence in Knowledge Management. Procedia Computer Science, 278, 149–158.
Sarmah, B., Hall, B., Rao, R., Patel, S., Pasquali, S., & Mehta, D. (2024). HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction. arXiv preprint arXiv:2408.04948.
Schuman, C. A., & Brent, A. C. (2005). Asset life cycle management: towards improving physical asset performance in the process industry. International Journal of Operations & Production Management, 25(6), 566–579.
Shi, D., Li, J., Meyer, O., & Bauernhansl, T. (2025). Enhancing retrieval-augmented generation for interoperable industrial knowledge representation and inference toward cognitive digital twins. Computers in Industry, 171, 104330.
Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, 157–169.
Tran, K.-T., Dao, D., Nguyen, M.-D., Pham, V., O'Sullivan, B., & Nguyen, H. (2025). Multi-Agent Collaboration Mechanisms: A Survey of LLMs. arXiv.
Wan, Y., Chen, Z., Liu, Y., Chen, C., & Packianather, M. (2025). Empowering LLMs by hybrid retrieval-augmented generation for domain-centric Q&A in smart manufacturing. Advanced Engineering Informatics, 65, 103212.
Wei, W., & Jiang, C. (2026). A multimodal large language model and knowledge graph-driven approach for intelligent product service. Computers & Industrial Engineering, 218, 112123.
Wooldridge, M., & Jennings, N. R. (1995). Intelligent agents: theory and practice. The Knowledge Engineering Review, 10(2), 115–152.
Wu, S., Xiong, Y., Cui, Y., Wu, H., Chen, C., Yuan, Y., Huang, L., Liu, X., Kuo, T.-W., Guan, N., & Xue, C. J. (2026). Retrieval-augmented generation for natural language processing: a survey. Artificial Intelligence Review, 59(9), 192.
Xu, Y., Li, M., Cui, L., Huang, S., Wei, F., & Zhou, M. (2020). LayoutLM: Pre-training of Text and Layout for Document Image Understanding. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Vol. pp. 1192–1200). Association for Computing Machinery.
Yang, R., Yang, B., Zhao, X., Gao, F., Feng, A., Ouyang, S., Blum, M., She, T., Jiang, Y., Lecue, F., Lu, J., & Li, I. (2025). Graphusion: A RAG Framework for Scientific Knowledge Graph Construction with a Global Perspective. In Companion Proceedings of the ACM on Web Conference 2025 (Vol. pp. 2579–2588). Association for Computing Machinery.
Yang, Y., Mu, W., Chen, Y., & Fang, X. (2026). An explainable question answering system for equipment maintenance with knowledge-enhanced LLM. Journal of Intelligent Manufacturing.
Zaim, H., Muhammed, S., & Tarim, M. (2019). Relationship between knowledge management processes and performance: critical role of knowledge utilization in organizations. Knowledge Management Research & Practice, 17(1), 24–38.
Zhang, W., Qin, C., Zhang, Y., & Zhang, W. (2026). AI-driven knowledge management system reconstruction: the CPOS four-dimensional collaborative framework. Journal of Knowledge Management, 1–38.
Zhao, W. X., Zhou, K., Li, J., Tang, T., Dong, Z., Hou, Y., Zhang, B., Min, Y., Zhang, J., Liu, P., Wang, X., Du, Y., Yang, C., Chen, Y., Chen, Z., Jiang, J., Ren, R., Li, Y., Tang, X.,…Wen, J.-R. (2026). A Survey of Large Language Models. Frontiers of Computer Science, 20(12), 2012627.