近來由於物聯網、大數據、人工智慧、5G、邊緣及雲端計算等新興資通訊技術快速發展，帶動工業製造場域的智能化。以現場設施的人工巡檢作業為例，透過擴增實境介面，以人工智慧協助辨識物件姿態，偵測裝置可能的異常狀態，啟動對應處理程序，可減少巡檢作業的工作負荷，降低過程中可能的人為疏失。基於此一概念，本研究以流體球閥開關為例，基於深度學習模型，發展偵測狀態異常的智能化工具。此外，分析不同條件下的部署方式，基於模糊運算輔助其規劃決策，考量計算模型（二維影像、三維姿態）、運算裝置（手持式裝置、邊緣裝置與雲端平台）、網路傳輸能力與介面裝置（手持式、頭戴式裝置）的組合影響；並根據不同產業特性，反映於計算時間、預測準確度與建置成本的需求，以及作業現場使用條件限制，建議可行之部署規劃組合；最後透過真實環境的部署與測試結果，驗證研究概念的可行性，作為導入擴增實境智能化應用的參考依據。

The rapid development of information and communication technologies, such as the Internet of Things, big data, artificial intelligence (AI), 5G, edge computing, and cloud computing lead to intelligentization in the manufacturing industry. For example, in manual inspection of on-site facilities, AI can assist human operator to detect anomaly of devices by recognizing object posture and to initiate the troubleshooting procedure via AR interfaces. The workload of the manual inspection and potential human errors are thus significantly reduced. Therefore, this work develops an intelligent solution based on deep learning models for automatic anomaly detection of ball valve switches. In addition, we analyze various factors that affect on-site deployment of the solution, including information types (2D image, 3D pose), computing devices (handheld, edge, cloud), network transmission, and interfacing devices (smartphone, AR goggle). A decision support tool based on fuzzy logics is constructed to assist the deployment planning. The support tool analyzes how the requirement of computational time, prediction precision, and implementation costs change with the characteristics of various industries. Based on the analysis result, it suggests feasible deployment plans subject to limitations of on-site conditions. Finally, test results obtained from real settings demonstrates the effectiveness of the support tool. This work provides valuable guidelines for implementing AR-based intelligent solutions.

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