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研究生： 簡梓婷
Tzu-Ting Chien
論文名稱： 結合IoT網路與人工智慧於即時追蹤隧道內機具狀態之研究
Research on Integrating IoT Networking and Artificial Intelligence for Real-time Tracking of Machinery Status Inside Tunnels
指導教授： 謝佑明
Yo-Ming Hsieh
口試委員: 陳鴻銘
Hung-Ming Chen
莊子毅
Tzu-Yi Chuang
謝佑明
Yo-Ming Hsieh
學位類別： 碩士
Master
系所名稱： 工程學院 - 營建工程系
Department of Civil and Construction Engineering
論文出版年： 2023
畢業學年度： 111
語文別： 中文
論文頁數： 100
中文關鍵詞： 工程機具 、物聯網 、機器學習 、數據分析 、線形無線感測器網路
外文關鍵詞： Construction Machinery, IoT, Machine Learning, Data Analysis, Linear Wireless Sensor Networks (LWSNs)
相關次數： 點閱：269  下載：12 
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    • 於隧道開挖過程中，機具需不斷向內深入，一項工程中也有多工作面同時進行的情況，不易掌握各機具工作情況，作業環境的實態監測中，若能即時追蹤機具狀態，並且將其統整量化像是工作時間等，雇主則可根據結果進行現場改善。
    本研究設計在每台隧道內工作之機具上安裝一自行開發之IoT裝置記錄機具之三軸向之加速度、三方向之角速度讀值等訊號，經過分析處理後推論出該機具目前之狀態後，將機具狀態透過藍牙發送，由IoT無線電收發站接收後向隧道洞口外發送，即可達成即時機具狀態的記錄。
    為了快速取得相關資料進行分析確認其可行性，本研究初期使用智慧型手機，撰寫其上之應用程式進行資料的採集，並將採集結果所得數據進行監督式機器學習分類，由辨別準確率來評估其可行性。
    本研究擬以低功耗長距離無線電LoRa建構隧道內之通訊網路，以將隧道內所收集到的資料傳出洞外至工務所供後續分析利用。為避免LoRa直接傳遞的廣播風暴不利於耗電量優化，及LoRaWAN星狀拓樸架構不利於隧道內線形傳遞，本研究自行開發網路並透過傳遞成功率來了解其可行性。

    During tunnel excavation, machinery needs to continuously advance inward. In projects with multiple faces active simultaneously, it becomes challenging to monitor the individual machinery operations effectively. Real-time tracking of machinery status in the context of operational environment monitoring, coupled with quantitative parameters such as working hours, could enable employers to make on-site improvements based on the results.
    In this study, we designed an IoT device to be installed on each piece of machinery working inside the tunnel. This device records signals such as three-axis acceleration and three-axis angular velocity of the machinery . After analyzing and processing these signals, the current state of the machinery is inferred. The machinery status is then transmitted via Bluetooth to an IoT wireless station, which forwards the information to the tunnel entrance. This achieves real-time recording and management of machinery status.
    To quickly obtain relevant data for analysis and confirm its feasibility, in the initial stage of this study, a smartphone was used. An application was developed on the smartphone for data collection. The collected data was then subjected to supervised machine learning classification to evaluate its feasibility based on recognition accuracy.
    This study aims to establish a communication network within the tunnel using Low-Power Wide-Area Network (LPWAN) technology, specifically Long Range (LoRa). This network will transmit the collected data from inside the tunnel to the engineering office for subsequent analysis. To mitigate data storms caused by direct LoRa transmission, which can negatively impact power consumption optimization, and to address the limitations of a LoRaWAN star topology that hinders linear transmission within the tunnel, this study has developed its own network architecture. The feasibility of this network is assessed through transmission success rates.

    論文摘要 I ABSTRACT II 目錄 IV 圖目錄 VII 表目錄 XI 第一章 緒論 1 1.1 研究動機與目的 1 1.2 研究流程 2 1.3 論文架構 4 第二章 文獻回顧 6 2.1 機具狀態識別 6 2.2 IOT組網 8 第三章 研究方法與工具 12 3.1 研究方法 12 3.1.1 機具狀態自動識別之可行性評估 12 3.1.2 工程概述 12 3.1.3 資料收集 13 3.1.4 決策樹訓練方法 14 3.2 硬體工具 18 3.2.1 智慧型手機 18 3.2.2 雷射測距儀 19 3.2.3 縮時攝影機 19 3.2.4 硬體開發版LoPy4與Heltec Wireless Stick 20 3.2.5 MPU9250 / MPU9255模組 21 3.2.6 18650鋰電池 22 3.3 程式開發工具 22 3.3.1 Android Studio 22 3.3.2 MATLAB 22 3.3.3 Visual Studio Code - Pymakr 22 3.3.4 Arduino IDE 1 & 2 23 第四章 透過IOT感測器監測機具狀態之可行性 24 4.1 現地機具狀態之收集 24 4.1.1 收集方式 24 4.1.2 收集前測試準備 24 4.1.3 己收集之數據與數量 25 4.2 資料分析 26 4.2.1 卡車 29 4.2.2 怪手 33 4.2.3 破碎機130 37 4.2.4 破碎機300 41 4.2.5 噴漿機 44 4.2.6 鑽堡機 47 4.3 IOT硬體耗電量測試 52 4.3.1 試驗設計 52 4.3.2 試驗結果 52 4.3.3 小結 53 4.4 小結 53 第五章 IOT技術於隧道內建立線形網路之可行性 56 5.1 低功耗藍牙之傳輸距離測試 56 5.1.1 試驗設計 56 5.1.2 試驗結果 57 5.1.3 小結 62 5.2 低功耗長距離無線電LORA之傳輸距離測試 64 5.2.1 試驗設計 64 5.2.2 距離試驗結果 65 5.2.3 小結 71 5.3 透過無線電LORA建立隧道內線形網路之可行性 72 5.3.1 LoRa長距離低功耗無線電概述 72 5.3.2 LoRa組網的困難與挑戰 72 5.3.3 以LoRa無線電組成線形網路之設計 75 5.3.4 LoRa網路及耗電量試驗結果 77 5.3.5 小結 78 5.4 小結 78 第六章 結論與建議 80 6.1 結論 80 6.2 建議與未來展望 81 參考文獻 83

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