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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99021完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李岳聯 | zh_TW |
| dc.contributor.advisor | Yueh-Lien Lee | en |
| dc.contributor.author | 黃仟如 | zh_TW |
| dc.contributor.author | Chien-Ju Huang | en |
| dc.date.accessioned | 2025-08-21T16:05:00Z | - |
| dc.date.available | 2025-08-22 | - |
| dc.date.copyright | 2025-08-21 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-07-30 | - |
| dc.identifier.citation | [1] Liu and Henry, “Pipeline | Definition, History, Types, Uses, & Facts,” Encyclopedia Britannica, Sep. 08, 1998. https://www.britannica.com/technology/pipeline-technology
[2] N. V. S. Korlapati, F. Khan, Q. Noor, S. Mirza, and S. Vaddiraju, “Review and analysis of pipeline leak detection methods,” Journal of Pipeline Science and Engineering, vol. 2, no. 4, p. 100074, Dec. 2022, doi: https://doi.org/10.1016/j.jpse.2022.100074. [3] T.-C. Su and M.-D. Yang, “Application of morphological segmentation to leaking defect detection in sewer pipelines,” Sensors, vol. 14, no. 5, pp. 8686–8704, May 2014, doi: 10.3390/s140508686. [4] Y.-L. Tsai, H.-C. Chang, S.-N. Lin, A.-H. Chiou, and T.-L. Lee, “Using convolutional neural networks in the development of a water pipe leakage and location identification system,” Applied Sciences, vol. 12, no. 16, p. 8034, Aug. 2022, doi: 10.3390/app12168034. [5] X. Yang, H. Fang, X. Yu, S. Li, and H. Liu, “Study on the mechanism of vibration signal generation in water supply pipeline leaks,” Measurement, vol. 229, p. 114415, Mar. 2024, doi: 10.1016/j.measurement.2024.114415. [6] Y. Li, Y. Zhou, M. Fu, F. Zhou, Z. Chi, and W. Wang, “Analysis of propagation and distribution characteristics of leakage acoustic waves in water supply pipelines,” Sensors, vol. 21, no. 16, p. 5450, Aug. 2021, doi: 10.3390/s21165450. [7] X. Yang, F. Wang, X. Yu, and S. Li, “Numerical and experimental study on propagation attenuation of leakage vibration acceleration signal of the buried water pipe,” Sustainability, vol. 14, no. 23, p. 16071, Dec. 2022, doi: 10.3390/su142316071. [8] P. Zhang et al., “Ground vibration analysis of leak signals from buried liquid-filled pipes: An experimental investigation,” Applied Acoustics, vol. 200, p. 109054, Oct. 2022, doi: 10.1016/j.apacoust.2022.109054. [9] D. J. Leslie and A. S. Tijsseling, “Wave speeds and phase velocities in liquid-filled pipes,” Brno, Czechia, Jan. 01, 1999. [Online]. Available: https://www.narcis.nl/publication/RecordID/oai%3Apure.tue.nl%3Apublications%2Fcf13618d-7f74-4ff2-ad53-420a7ae9de42 [10] S. Li, Y. Wen, P. Li, J. Yang, X. Dong, and Y. Mu, “Leak location in gas pipelines using cross-time–frequency spectrum of leakage-induced acoustic vibrations,” Journal of Sound and Vibration, vol. 333, no. 17, pp. 3889–3903, May 2014, doi: 10.1016/j.jsv.2014.04.018. [11] A. Huber, Numerical Modeling of Guided Waves in Anisotropic Composites with Application to Air-coupled Ultrasonic Inspection. 2020. [12] N. Ullah, Z. Ahmed, and J.-M. Kim, “Pipeline leakage detection using acoustic emission and machine learning algorithms,” Sensors, vol. 23, no. 6, p. 3226, Mar. 2023, doi: 10.3390/s23063226. [13] H. Liu, H. Fang, X. Yu, and Y. Xia, “Adaptive signal extraction based on RLMD-SVD for water pipeline leakage localization,” IEEE Sensors Journal, vol. 24, no. 16, pp. 26522–26533, Aug. 2024, doi: 10.1109/jsen.2024.3422385. [14] J. Lu et al., “Adaptive denoising method for leakage detection of liquid pipelines using Automatic Variational Mode Decomposition,” Journal of the Franklin Institute, vol. 362, no. 2, p. 107475, Dec. 2024, doi: 10.1016/j.jfranklin.2024.107475. [15] H. Shukla and K. Piratla, “Leakage detection in water pipelines using supervised classification of acceleration signals,” Automation in Construction, vol. 117, p. 103256, May 2020, doi: 10.1016/j.autcon.2020.103256. [16] C. Spandonidis, P. Theodoropoulos, F. Giannopoulos, N. Galiatsatos, and A. Petsa, “Evaluation of deep learning approaches for oil & gas pipeline leak detection using wireless sensor networks,” Engineering Applications of Artificial Intelligence, vol. 113, p. 104890, May 2022, doi: 10.1016/j.engappai.2022.104890. [17] S. Tariq, B. Bakhtawar, and T. Zayed, “Data-driven application of MEMS-based accelerometers for leak detection in water distribution networks,” The Science of the Total Environment, vol. 809, p. 151110, Oct. 2021, doi: 10.1016/j.scitotenv.2021.151110. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99021 | - |
| dc.description.abstract | 本論文提出了一種基於聲學方法的低成本無線感測器,旨在實現PVC管線洩漏點的精準檢測與定位。
有鑑於管線洩漏對水資源、能源運輸及環境保護造成的重大影響,本研究結合三軸加速規與無線傳輸技術,設計並搭建了一套模擬實際管線洩漏情境的實驗平台。透過滑動窗口、互相關、快速傅立葉轉換(FFT)、短時傅立葉轉換(STFT)以及離散小波轉換(DWT)等信號處理方法,結合色散曲線與到達時間差(TDOA)技術,本研究能夠有效分析洩漏引發的振動訊號,並估算洩漏點的空間位置。實驗採用六分PVC管系統,模擬不同洩漏位置,驗證了所提方法的有效性。 結果顯示,互相關方法在短距離管線中的定位準確度優於小波轉換,平均誤差率較低,尤其在時域分析中表現穩定。然而,小波轉換在處理非平穩訊號時具備理論優勢,但受限於頻率解析度及計算複雜度,實際應用效果不如預期。本研究進一步比較了各方法的計算複雜度與適用情境,分析其在不同管線條件下的表現差異。 針對未來發展,本研究提出可從管道長度、材質(如金屬管與非金屬管的差異)、洩漏孔徑以及環境因素等面向進行延伸實驗,以提升聲學檢測技術的穩健性與實用性,為管線洩漏監測提供更高效且經濟的解決方案。 | zh_TW |
| dc.description.abstract | This thesis proposes a low-cost wireless detection device based on acoustic methods for the precise detection and localization of leaks in PVC pipelines.
Given the significant impact of pipeline leaks on water resources, energy transportation, and environmental protection, this study integrates triaxial accelerometers with wireless transmission technology to design and construct an experimental platform simulating real-world pipeline leak scenarios. By employing signal processing techniques such as sliding window, cross-correlation, Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), and Discrete Wavelet Transform (DWT), combined with dispersion curves and Time Difference of Arrival (TDOA) methods, this research effectively analyzes vibration signals induced by leaks and estimates the spatial location of leak points. Experiments were conducted using a 3/4-inch PVC pipeline system, simulating various leak positions to validate the proposed method’s effectiveness. Results demonstrate that the cross-correlation method outperforms wavelet transform in localization accuracy for short-distance pipelines, exhibiting lower average error rates, particularly in time-domain analysis where it shows stable performance. However, while wavelet transform holds theoretical advantages in processing non-stationary signals, its practical performance is limited by frequency resolution and computational complexity. This study further compares the computational complexity and applicability of each method, analyzing their performance differences under varying pipeline conditions. For future development, the study suggests extending experiments to explore factors such as pipeline length, material (e.g., differences between metallic and non-metallic pipes), leak aperture, and environmental factors to enhance the robustness and practicality of acoustic detection technology, thereby providing a more efficient and cost-effective solution for pipeline leak monitoring. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-21T16:05:00Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-21T16:05:00Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTRACT iii 目次 v 圖次 viii 表次 ix Chapter 1 緒論 1 1.1 研究動機與目的 1 1.2 論文架構 3 Chapter 2 文獻回顧 4 2.1 台灣的管線洩漏檢測研究回顧 4 2.2 洩漏引發的振動與傳播機制 4 2.2.1 管線的洩漏振動傳播 5 2.2.2 管線洩漏訊號之衰減 5 2.3 色散曲線 5 2.4 加速規之量測原理 7 2.4.1 加速度感測的技術規格與工作原理 7 2.4.1.1 技術規格 7 2.4.1.2 工作原理 8 2.4.2 訊號處理與特徵提取方法 8 2.4.2.1 時域處理方法 9 2.4.2.2 頻域處理方法 9 2.4.2.3 時頻域處理方法 9 2.4.2.4 其他方法 9 Chapter 3 背景理論 10 3.1 滑動窗口(Sliding Window) 10 3.2 互相關(Cross-Correlation) 10 3.3 快速傅立葉轉換(Fast Fourier Transform, FFT) 11 3.4 短時傅立葉轉換(Short-Time Fourier Transform, STFT) 11 3.5 小波轉換(Wavelet Transform) 11 3.5.1 連續小波變換(Continuous Wavelet Transform, CWT) 12 3.5.2 離散小波變換(Discrete Wavelet Transform,DWT) 12 3.6 計算複雜度比較 12 3.7 到達時間差(Time Difference of Arrival, TDOA) 13 Chapter 4 研究方法 14 4.1 實驗器材 14 4.2 實驗平台設計 15 4.3 感測器設計與運作 16 4.3.1 硬體設計 16 4.3.2 程式設計 16 4.4 實驗流程 19 4.4.1 實驗條件 19 4.4.2 實驗步驟 19 4.5 信號處理與洩漏定位 19 4.5.1 數據載入 19 4.5.2 信號處理 20 Chapter 5 實驗結果 22 5.1 實驗結果與分析 22 5.2 分析方法比較 25 5.2.1 小波轉換 25 5.2.2 互相關 28 Chapter 6 未來展望 29 6.1 管道長度 29 6.2 管道材質 29 6.3 洩漏孔徑 29 6.4 環境因素 29 參考文獻 30 附錄 32 附錄A. 各分析方法詳細數據 32 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | PVC管線 | zh_TW |
| dc.subject | 洩漏檢測 | zh_TW |
| dc.subject | 聲學方法 | zh_TW |
| dc.subject | 三軸加速規 | zh_TW |
| dc.subject | 信號處理 | zh_TW |
| dc.subject | 互相關 | zh_TW |
| dc.subject | 小波轉換 | zh_TW |
| dc.subject | leak detection | en |
| dc.subject | wavelet transform | en |
| dc.subject | cross-correlation | en |
| dc.subject | signal processing | en |
| dc.subject | triaxial accelerometer | en |
| dc.subject | acoustic method | en |
| dc.subject | PVC pipeline | en |
| dc.title | PVC 管線洩漏聲訊號檢測方法之設計與實驗驗證 | zh_TW |
| dc.title | Design and Experimental Validation of an Acoustic Signal Method for Leak Detection in PVC Pipelines | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 陳昭宏;鄭憶中 | zh_TW |
| dc.contributor.oralexamcommittee | Jau-Horng Chen;I-Chung Cheng | en |
| dc.subject.keyword | PVC管線,洩漏檢測,聲學方法,三軸加速規,信號處理,互相關,小波轉換, | zh_TW |
| dc.subject.keyword | PVC pipeline,leak detection,acoustic method,triaxial accelerometer,signal processing,cross-correlation,wavelet transform, | en |
| dc.relation.page | 41 | - |
| dc.identifier.doi | 10.6342/NTU202502852 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2025-08-01 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 工程科學及海洋工程學系 | - |
| dc.date.embargo-lift | 2025-08-22 | - |
| 顯示於系所單位: | 工程科學及海洋工程學系 | |
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