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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/92619完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 宋家驥 | zh_TW |
| dc.contributor.advisor | Chia-Chi Sung | en |
| dc.contributor.author | 黃奕忠 | zh_TW |
| dc.contributor.author | Yi-Chung Huang | en |
| dc.date.accessioned | 2024-05-14T16:06:01Z | - |
| dc.date.available | 2024-05-15 | - |
| dc.date.copyright | 2024-05-14 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-05-07 | - |
| dc.identifier.citation | [1] 吳冠逸、雲在天、徐一仁、馬開東、江茂雄. 浮動式風機「臺大浮臺」之設計與技術開發. 臺灣風能學術研討會, 12 2023.
[2] J. Renato M. de Sousa, C. S. de Aguiar, G. B. Ellwanger, E. C. Porto, D. Foppa, andJr. de Medeiros, Cipriano José. Undrained Load Capacity of Torpedo Anchors Em bedded in Cohesive Soils. Journal of Offshore Mechanics and Arctic Engineering, 133(2):021102, 11 2010. [3] P. Robertson and K Cabal. Cone penetration testing guide to. 05 2023. [4] C. O’Beirne, C. O’Loughlin, and C. Gaudin. Assessing the penetration resistance acting on a dynamically installed anchor in normally and over consolidated clay. Canadian Geotechnical Journal, 54, 08 2016. [5] S. Keerthi Raaj, N.Saha, and R. Sundaravadivelu. Freefall hydrodynamics of tor pedo anchors through experimental and numerical analysis. Ocean Engineering, 243:110213, 2022. [6] A. Blake, C. O’Loughlin, J. Morton, Colm O’Beirne, Christophe Gaudin, and David White. In situ measurement of the dynamic penetration of free-fall projectiles in soft soils using a low-cost inertial measurement unit. Geotechnical Testing Journal, 39:20140135, 03 2016. [7] L. Yu-Sheng, W. Hsuan-Wen, and L. Sheng-Hao. An integrated accelerometer for dynamic motion systems. Measurement, 125:471–475, 2018. [8] L. Huaizhong. A method of dual-sensor signal fusion for dsp-based wide-range vibration detection and control. Measurement, 69:72–80, 2015. [9] C.E. Shannon. Communication in the presence of noise. Proceedings of the IRE, 37(1):10–21, 1949. [10] K. Salahshoor, M. Mosallaei, and M. Bayat. Centralized and decentralized process and sensor fault monitoring using data fusion based on adaptive extended kalman filter algorithm. Measurement, 41(10):1059–1076, 2008. [11] D. Nemec, J. Andel, V. Simak, and J. Hrbcek. Homogeneous sensor fusion opti mization for low-cost inertial sensors. Sensors, 23(14), 2023. [12] J. H. Hetherington. Observations on the statistical iteration of matrices. Phys. Rev. A, 30:2713–2719, 1984. [13] R. E. Kalman. A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, 82(1):35–45, 03 1960. [14] B-A. McElhoe. An assessment of the navigation and course corrections for a manned flyby of mars or venus. IEEE Transactions on Aerospace and Electronic Systems, AES-2(4):613–623, 1966. [15] S.J. Julier, J.K. Uhlmann, and H. Durrant-Whyte. A new approach for filtering non linear systems. Proceedings of the American Control Conference, pages 1628–1632, 1995. [16] P. Sage Andrew and W. Husa Gary. Adaptive filtering with unknown prior statistics. IEEE Transactions on Automatic Control, pages 760–769, 1969. [17] Silicon Designs. SPECIALTY +5V DC MODELS 2012 2422 LOW VOLTAGE MEMS DC ACCELEROMETERS, 2022. [18] Raspberry Pi Ltd. Raspberry Pi Zero 2 W, 2021. [19] Texas Instruments. ADS126x 32-Bit, Precision, 38-kSPS, Analog-to-Digital Converter (ADC) with Programmable Gain Amplifier (PGA) and Voltage Reference, 2021. [20] Microsoft Coroporation. Remote Network Driver Interface Specification (RNDIS) Protocol, 2014. [21] L. Drolet, F. Michaud, and J. Cote. Adaptable sensor fusion using multiple kalman filters. 2:1434–1439 vol.2, 2000. [22] A. Smyth and W. Meiliang. Multi-rate kalman filtering for the data fusion of dis placement and acceleration response measurements in dynamic system monitoring. Mechanical Systems and Signal Processing, 21(2):706–723, 2007. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/92619 | - |
| dc.description.abstract | 近年來,國際間對於能源議題討論熱烈,並且積極推動綠色能源的發展,在風力發電方面,由於我國在陸域發展上不僅土地受限,並且會造成風場周圍民眾居住上的困擾,因此在政府的決策及推動下,發展離岸式風力發電。在離岸風電的開發評估上,會對於當地的環境因素決定適合的地點,其中部分裝置是透過對於自落運動方式進行量測或安裝,例如土壤取樣器、重力錨等,若將其運動過程之加速訊號列入考量,可推算出其他與其相關變數之評估資訊。
本研究致力於研發具有高精度、大範圍加速度量測、輕量體積小及低成本之加速計量測系統,使其能夠更廣泛應用於自落式裝置之量測。本文第一章節會針對離岸風力發電目前在發展方向以及需要的量測特性進行討論; 第二章會透過前人所發表之相關文章做文獻回顧及探討技術,並且介紹本研究所使用到的一些原理; 第三章介紹量測系統整合,包括硬體間整合之取捨、針對同質多加速度量測所設計之卡爾曼濾波算法及相關操作機制; 在最後章節構建之系統以 MATLABSimulink 模擬其算法性能,並且進行現地實驗,將量測系統實作安裝於重力式模型錨上,於投放過程紀錄及算法處理,取得重力式模型錨之運動過程及位置,並與精密量測儀器之積分結果比對,其加速度最大值落在百分之三以內的平均誤差以及貫入深度落在百分之六以內的平均誤差,實現低成本、高精度及大加速度範圍量測之小型系統。 | zh_TW |
| dc.description.abstract | In recent years, there has been a heated discussion on energy issues internationally, with a strong emphasis on promoting the development of green energy. In the field of wind power generation, due to land constraints and the potential disturbance to local residents caused by onshore development, offshore wind power has been actively promoted by governments. In the evaluation of offshore wind power development, suitable locations are determined based on local environmental factors. Some devices, such as soil samplers and gravity anchors, are measured or installed through their free-fall motion. Taking into account the acceleration signals of their motion processes can provide valuable information for evaluating other related variables.
This study aims to develop an acceleration measurement system with high precision, wide range, compact size, and low cost, making it more widely applicable to measurements on self-deployed devices. The first chapter of this paper discusses the current development direction and measurement characteristics required for offshore wind power generation. The second chapter conducts a literature review and discusses the technologies previously published, introducing some principles used in this study. The third chapter introduces the integration of the measurement system, including hardware integration decisions, the Kalman filtering algorithm designed for homogeneous multi-acceleration measurement, and related operational mechanisms. In the final chapter, the system constructed is simulated for its algorithm performance using MATLAB Simulink and is subjected to on-site experiments. The measurement system is implemented and installed on a gravity-modeled anchor, recording and processing data during deployment to obtain the motion processes and positions of the anchor. The results are compared with the integration results of precision measuring instruments, showing that the maximum acceleration values have an average error within three percent and the penetration depth has an average error within six percent, achieving low-cost, high-precision, and wide-range acceleration measurement in a compact system. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-05-14T16:06:00Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-05-14T16:06:01Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員審定書 i
致謝 ii 摘要 iii Abstract iv 目次 vi 圖次 ix 表次 xii 符號次 xiii 第一章 緒論 1 1.1 研究背景及動機 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 論文架構 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 第二章 文獻回顧 5 2.1 重力錨之數據量測 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 加速度計種類及其量測原理 . . . . . . . . . . . . . . . . . . . . . . 7 2.2.1 壓電式加速度計 . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2.2 力平衡式加速度計 . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2.3 電容式加速度計 . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.4 壓阻式加速度計 . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3 數據採樣流程及相關原理 . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3.1 類比數位轉換 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3.2 取樣定理 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3.3 數位訊號處理 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.4 感測器融合理論介紹 . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.4.1 數據融合及感測器整合系統 . . . . . . . . . . . . . . . . . . . . 14 2.4.2 感測器融合 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.4.3 粒子濾波器 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.4.4 卡爾曼濾波器 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.4.5 延伸導航算法介紹 . . . . . . . . . . . . . . . . . . . . . . . . . . 21 第三章 研究方法及系統設計開發 27 3.1 研究流程 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2 硬體配置及整合 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2.1 感測器系統 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.2.2 樹莓派微處理器 . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.2.3 類比數位轉換晶片 (A/D Converter) . . . . . . . . . . . . . . . . . 30 3.2.4 SPI 通訊介面 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.3 數據採集程式設計 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.3.1 數據採集流程 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.3.2 SSH 連線 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.3.3 無網路連線樹莓派方法 . . . . . . . . . . . . . . . . . . . . . . . 34 3.3.4 採集程式與資料存儲 . . . . . . . . . . . . . . . . . . . . . . . . 35 3.3.5 GPIO 中斷機制 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.3.6 平行程式設計 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.4 演算法設計及其原理 . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.4.1 頻率域濾波 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.4.2 決策融合算法及卡爾曼濾波 . . . . . . . . . . . . . . . . . . . . 40 3.4.3 演算法架構 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 第四章 演算法模擬︑室內試驗及結果 43 4.1 演算法模擬 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.1.1 Simulink 模擬系統建置 . . . . . . . . . . . . . . . . . . . . . . . 43 4.1.2 模擬訊號及結果 . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.2 室內試驗及結果 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.2.1 室內試驗 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.2.2 實驗結果與討論 . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 第五章 結論 54 第六章 未來工作 56 參考文獻 57 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 數據採集系統 | zh_TW |
| dc.subject | 大範圍量測 | zh_TW |
| dc.subject | 卡爾曼濾波 | zh_TW |
| dc.subject | 加速度計 | zh_TW |
| dc.subject | ARM 微處理器 | zh_TW |
| dc.subject | ARM Processor | en |
| dc.subject | Extended Kalman Filter | en |
| dc.subject | Data Acquisition System | en |
| dc.subject | Wide Range Measurement | en |
| dc.subject | Accelerometer | en |
| dc.title | 應用於自落式裝置之小型大範圍加速度量測系統研發 | zh_TW |
| dc.title | Development of Compact Wide-Range Acceleration Measurement System Applied to Free-Falling Devices | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 林俊宏;陳彥廷 | zh_TW |
| dc.contributor.oralexamcommittee | Chun-Hung Lin;Yen-Ting Chen | en |
| dc.subject.keyword | 加速度計,大範圍量測,ARM 微處理器,數據採集系統,卡爾曼濾波, | zh_TW |
| dc.subject.keyword | Accelerometer,Wide Range Measurement,ARM Processor,Data Acquisition System,Extended Kalman Filter, | en |
| dc.relation.page | 59 | - |
| dc.identifier.doi | 10.6342/NTU202400893 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2024-05-08 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 工程科學及海洋工程學系 | - |
| 顯示於系所單位： | 工程科學及海洋工程學系 | |
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