在現行製造業中，設備監控多依賴人工巡檢與經驗判斷，難以即時掌握設備狀態、判斷異常並提出有效調適建議，導致非計畫性停機。設備的穩定性直接影響生產效率與產品品質。操作人員主要依靠定期巡檢與經驗判斷來發現問題，當設備出現異常時，需要較長時間才能診斷根因，導致生產延誤與成本損失。更關鍵的是，每次故障的診斷與處理經驗難以系統化積累，相似的問題再次發生時，無法參考過去的解決方案，使得設備管理效率難以提升。
本研究以單層浪板滾輪成型機為研究對象，建構一套整合數位孿生與智慧代理人的設備監控系統。本系統採用多代理人架構，涵蓋監控代理人與對話代理人兩大代理人。其中，監控代理人底下包含現況分析、趨勢分析、異常偵測與根因分析四類子代理人，分別負責進行設備狀態監測、參數變化、異常現象判斷與根因推論；對話代理人則與操作人員進行自然語言互動，提供診斷說明。
在系統實作上，本研究以深度學習模型進行異常偵測，並以有向無環圖（Directed Acyclic Graph, DAG）與順序功能圖（Sequential Function Chart, SFC）作為根因分析之知識架構。此外，系統結合檢索增強生成（Retrieval-Augmented Generation, RAG）技術，從知識庫中檢索歷史相似案例與調適建議。數位孿生模型則即時映射實體設備之運作狀態，並使設備能進行自主調適。
實驗結果顯示，本系統能有效提升設備貢獻度，縮短診斷時間。本研究成果驗證了數位孿生結合生成式 AI 技術在智慧製造應用中的可行性，為製造業設備監控與智慧化提供可參考的實踐方案。

In modern manufacturing, equipment monitoring still relies on manual inspection and experience-based judgment, making it difficult to assess equipment conditions in real time, detect abnormalities promptly, and provide adjustment suggestions. Delayed fault diagnosis may lead to unplanned downtime, production delays, and additional costs. Moreover, diagnostic and corrective experiences are often not systematically recorded or reused, causing similar problems to require repeated troubleshooting.
This study develops an equipment monitoring system for a single-layer corrugated sheet roll forming machine by integrating digital twin technology and intelligent agents. The proposed system adopts a multi-agent architecture composed of a monitoring agent and a conversational agent. The monitoring agent consists of four sub-agents: current-state analysis, trend analysis, anomaly detection, and root cause analysis, which are responsible for condition monitoring, parameter trend analysis, abnormality identification, and root cause inference. The conversational agent interacts with operators through natural language and provides diagnostic explanations. In the system, a deep learning model is used for anomaly detection, while a Directed Acyclic Graph (DAG) and a Sequential Function Chart (SFC) serve as the knowledge framework for root cause analysis. Retrieval-Augmented Generation (RAG) is applied to retrieve similar historical cases and adjustment suggestions from the knowledge base. The digital twin model reflects the operating status of the physical equipment in real time and enables autonomous adjustment of the equipment.
Experimental results show that the system improves equipment utilization and reduces diagnosis time. These results demonstrate the integration of digital twin technology and generative AI for equipment monitoring in intelligent manufacturing, providing a practical reference for fault diagnosis and knowledge reuse in manufacturing environments.

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