近年來，物聯網的廣泛運用，使得飛機上布建了大量的感測器幫助飛行員掌握飛行現況。但相對飛機維修而言，因為大量感測器造成龐大資料量，導致維修時程大幅延長。目前飛機使用年限是根據飛機靜力實驗的最壞狀況進行推估，與現實服役時的年限相差甚遠。因此，如何精進飛機服役壽命分析，成為機隊管理的重要環節。此外，飛機在飛行過程中，機體應力的變化，常是飛機發生重大事故的前兆。換言之，如何在飛機上，透過應力感測器實現即時量測與非即時的預診，實為飛機飛行安全的重要防護措施之一。

本研究所研製的系統是建置在FPGA中，並實現邊緣運算的物聯網設備。其中，藉由布建在金屬板上的13支應力感測器並將其連接到其類比訊號處理電路，經訊號放大後，再讀取到FPGA中。整體系統包含即時狀況警示與離線預診分析等兩種模式。在即時狀況警示模式中，運用峰谷分割演算法將原始資料透過閥值判斷資料是否需要即時回傳到飛航系統中加以警示。在離線預診分析模式中，當飛航結束後，透過無線傳送所有應力的觸發矩陣到遠端的診斷電腦中，並藉由資料圖形化分析程式將所有接收到的資料圖形化顯示，以利維修人員分析整體飛機上的應力資訊。

最後，本研究實現了具備IoT邊緣運算之飛機應力即時量測與預診系統，且經實測，其運算後的資料與原始資料相比，不但大幅縮減了維修資料下載的時間，更減少平均50%左右的資料量的資料傳輸，達到即時警示的功能。其中本系統在執行演算法後的最差情況，只需要4.35126ms即可執行完畢13支應力感測器的分析資料。藉由實驗證明此系統 以FPGA為架構來研製IoT邊緣運算之飛機應力即時量測與預診系統，不僅可銜接至現有的航電系統中，並能省去重新開發時間與節省硬體成本。

In recent years, the widespread use of the Internet of Things has enabled a large number of sensors to be deployed on aircraft to help pilots grasp the status of the flight. However, compared with aircraft maintenance, the large amount of data caused by a large number of sensors has greatly extended the maintenance schedule. The current aircraft service life is estimated based on the worst case of the aircraft static experiment, which is far from the actual service life.Therefore, how to improve aircraft service life analysis has become an important part of fleet management. In addition, changes in the body stress of an aircraft during flight are often a precursor to a major accident. In other words, how to achieve real-time measurement and non-real-time pre-diagnosis through Strain Gages on an aircraft is actually one of the important protective measures for aircraft flight safety.
The system developed in this research is an IoT device that is built in FPGA and realizes edge computing. Among them, 13 Strain Gages are deployed on the metal plate and connected to its analog signal processing circuit, after the signal is amplified, it is read into the FPGA. The overall system includes two modes: real-time condition alert and offline pre-diagnosis analysis. In the real-time condition alert mode, the Peak Valley segmentation algorithm is used to determine whether the original data needs to be sent back to the flight system in real time for warning through the threshold value. In the offline pre-diagnosis analysis mode, when the flight is over, the trigger matrix of all stresses is transmitted wirelessly to the remote diagnosis computer, and all the received data is displayed graphically by the data graphical analysis program to facilitate The maintenance personnel analyze the stress information on the entire aircraft.
Finally, this research has implemented The Aircraft Stress Real-Time Measurement and Pre-diagnosis System with IoT Edge Computing. Compared with the original data, the calculated data has not only greatly reduced the time for downloading maintenance data, but also reduced an average of 50%. Data transmission with a data volume of about% achieves the function of real-time warning. Among them, the worst case of the system after executing the algorithm, it only takes 4.35126ms to complete the analysis data of 13 Strain Gages. Experiments show that this system uses FPGA as the framework to develop an IoT edge computing aircraft stress measurement and pre-diagnosis system, which can not only be connected to the existing avionics system, but also saves time for redevelopment and saves hardware costs.

摘要...i
Abstract...ii
誌謝...iv
目錄...v
表目錄...vii
圖目錄...viii
第一章 緒論...1
1.1 研究背景...1
1.2 研究目的...1
1.3 國內外相關之研究情況...2
1.4 論文章節概述...3
第二章 系統原理...4
2.1 FPGA概念...4
2.1.1 FPGA架構...4
2.2 UART通訊協定...5
2.3 SPI通訊協定...6
2.3.1 SPI模式...6
2.4 峰谷分割演算法(PEAK-VALLEY SEGMENTATION ALGORITHM)...7
2.4.1 峰谷分割演算法說明...7
2.5 STRAIN GAGE應力感測器...8
2.5.1 壓阻式應力感測器工作原理...8
2.5.2 應力感測器電阻變化率...9
2.6 邊緣運算...10
2.6.1 邊緣運算的特點...10
2.6.2 邊緣運算的實現方式...11
第三章 系統設計...12
3.1 系統架構...12
3.2 應力感測器類比訊號處理電路...14
3.2.1 KFG-6-120-C1-11 L1M2R元件...18
3.2.2 LTC2351元件...18
3.3 FPGA應力感測器邊緣運算模組...19
3.4 IOT雲端資料傳輸模組...27
3.5 資料圖形化分析程式...28
第四章 系統實驗與驗證...30
4.1 整體系統驗證架構...30
4.2 驗證峰谷分割演算法可行性評估...31
4.3 驗證應力感測器類比訊號處理電路...32
4.4 IOT雲端資料傳輸模組穩定性之驗證...36
4.5 即時狀況警示模式之驗證...36
4.6 離線預診分析模式之驗證...38
4.7 應力感測器之應力變化驗證...40
第五章 結論與未來展望...42
5.1 結論...42
5.2 未來展望...42
參考文獻...44
Extended Abstract...47

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