請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81782完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 宋家驥(Chia-Chi Sung) | |
| dc.contributor.author | Po-Yu Huang | en |
| dc.contributor.author | 黃柏諭 | zh_TW |
| dc.date.accessioned | 2022-11-25T03:03:29Z | - |
| dc.date.available | 2023-08-01 | |
| dc.date.copyright | 2021-11-11 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-08-20 | |
| dc.identifier.citation | [1] REN21, “Renewable Energy Policy Network for the 21st Century.” Available: https://www.ren21.net/wp-content/uploads/2019/05/gsr_2020_full_report_en.pdf. [2] 經濟部能源局，〈能源統計資料查詢系統：109年發電結構〉。檢自：www.moeaboe.gov.tw/wesnq/Views/B01/wFrmB0102.aspx?fbclid=IwAR20GtPnHYFIOjFBfy4mzma5_mmthMXNsi70bggn5wqIB5Pm1Dtjm_8LNMg。 [3] 4C Offshore, “Global Offshore Wind Speeds Rankings.” Available: https://www.4coffshore.com/windfarms/windspeeds.aspx. [4] GWEC, “Global Wind Power Growth Must Triple over Next Decade to Achieve Net Zero.” Available: https://gwec.net/global-wind-power-growth-must-triple-over-next-decade-to-achieve-net-zero. [5] The Welding Institute, “How Long do Wind Turbines Last?” Available: https://www.twi-global.com/technical-knowledge/faqs/how-long-do-wind-turbines-last. [6] C. Röckmann, S. Lagerveld, and J. Stavenuiter, “Operation and Maintenance Costs of Offshore Wind Farms and Potential Multi-use Platforms in the Dutch North Sea,” in Aquaculture Perspective of Multi-Use Sites in the Open Ocean, New York, NY, USA: Springer, 2017, pp. 97-113. [7] G. Jiang, H. He, J. Yan, and P. Xie, “Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox,” IEEE Transactions on Industrial Electronics, vol. 66, no. 4, pp. 3196-3207, 2018. [8] S. Sheng, “Gearbox Reliability Database: Yesterday, Today, and Tomorrow (Presentation),” presented at Wind Turbine Tribology Seminar, Chicago, IL, USA, Oct. 29-31, 2014. [9] H. Ahmed, and A. K. Nandi, Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines, Hoboken, NJ, USA: Wiley-IEEE Press, 2019. [10] V. Sharma, and A. Parey, “A Review of Gear Dault Diagnosis Using Various Condition Indicators,” Procedia Engineering, vol. 144, pp. 253-263, 2016. [11] G. A. Skrimpas, et al., “Residual Signal Feature Extraction for Gearbox Planetary Stage Fault Detection,” Wind Energy, vol. 20, no. 8, pp. 1389-1404, 2017. [12] P. D. McFadden, “A Revised Model for the Extraction of Periodic Waveforms by Time Domain Averaging,” Mechanical Systems and Signal Processing, vol. 1, no. 1, pp. 83-95, 1987. [13] S. K. Roy, A. R. Mohanty, and C. S. Kumar, “Fault Detection in a Multistage Gearbox by Time Synchronous Averaging of the Instantaneous Angular Speed,” Journal of Vibration and Control, vol. 22, no. 2, pp. 468-480, 2016. [14] N. E. Huang, et al., “The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis,” Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903-995, 1998. [15] B. Cahyono, D. Priyanta, and F.R.F. Ramadhan, “Vibration Spectrum Analysis for Indicating Damage on Turbine and Steam Generator Amurang Unit 1,” International Journal of Marine Engineering Innovation and Research, vol. 2, no. 1, 2017. [16] B. P. Bogert, “The Quefrency Analysis of Time Series for Echoes; Cepstrum, Pseudo-autocovariance, Cross-cepstrum and Saphe Cracking,” Time series analysis, vol. 15, pp. 209-243, 1963. [17] Y. Yang, D. Yu, and J. Cheng, “ A Fault Diagnosis Approach for Roller Bearing Based on IMF Envelope Spectrum and SVM,” Measurement, vol. 40, no. 9-10, pp. 943-950, 2007. [18] A. Haar, “Zur Theorie der Orthogonalen Funktionensysteme,” Mathematische Annalen, vol. 71, pp. 38-53, 1911. [19] A. Grossmann, and J. Morlet, “Decomposition of Hardy Functions into Square Integrable Wavelets of Constant Shape,” SIAM Journal on Mathematical Analysis, vol. 15, no. 4, pp. 723-736, 1984. [20] S. G. Mallat, “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp. 674-693, 1989. [21] J. Lin, and M. J. Zuo, “Gearbox Fault Diagnosis Using Adaptive Wavelet Filter,” Mechanical Systems and Signal Processing, vol. 17, no. 6, pp. 1259-1269, 2003. [22] C. Cortes, and V. Vapnik, “Support-vector networks,” Machine learning, vol. 20, no. 3, pp. 273-297, 1995. [23] W. S. McCulloch, and W. Pitts, “A Logical Calculus of the Ideas Immanent in Nervous Activity,” The Bulletin of Mathematical Biophysics, vol. 5, no. 4, pp. 115-133, 1943. [24] P. J. Werbos, “Backpropagation Through Time: What It Does and How to Do It,” Proc. IEEE, vol. 78, no. 10, pp. 1550-1560, 1990. [25] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based Learning Applied to Document Recognition,” Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, 1998. [26] A. Ragheb and M. Ragheb, “Wind Turbine Gearbox Technologies,” in 2010 1st International Nuclear and Renewable Energy Conference (INREC), Amman, JO, pp. 1-8, 2010. [27] Wind Energy Database, “Manufacturers and Turbines: Siemens SWT-4.0-130.” Available: https://www.thewindpower.net/turbine_en_957_siemens_swt-4.0-130.php. [28] P. Tchakoua, et al., “Wind Turbine Condition Monitoring: State-of-the-art Review, New Trends, and Future Challenges,” Energies, vol. 7, no. 4, pp. 2595-2630, 2014. [29] R. Anslow, and D. O’Sullivan, “Choosing the Best Vibration Sensor for Wind Turbine Condition Monitoring,” Analog Dialogue, vol. 54, no. 3, pp. 1-6, 2020. [30] P. D. McFadden, “Detecting Fatigue Cracks in Gears by Amplitude and Phase Demodulation of the Meshing Vibration, Journal of Vibration, Acoustics, Stress, and Reliability in Design, vol. 108, no. 2, pp. 165-170, 1986. [31] R. B. Randall, “A New Method of Modeling Gear Faults,” Journal of Mechanical Design, vol. 104, no. 2, pp. 259-267, 1982. [32] P. D. McFadden, and J.D. Smith, “An Explanation for the Asymmetry of the Modulation Sidebands About the Tooth Meshing Frequency in Epicyclic Gear Vibration,” Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 199, no. 1, pp. 65-70, 1985. [33] B. Azizi, W. R. Shahkarami, F. Dalvand, S. Dalvand, and A. Khorsandi, “Development of Current Noise Cancellation Method Using Sample Delaying for Bearing Fault Diagnosis in Induction Motors,” in 2018 5th International Conference on Electrical and Electronics Engineering, Istanbul, TR, pp. 445-453. [34] A. R. Mohanty, and C. Kar, “Fault Detection in a Multistage Gearbox by Demodulation of Motor Current Waveform,” IEEE Transactions on Industrial Electronics, vol. 53, no. 4, pp. 1285-1297, 2006. [35] J. D. Wu, and C. H. Liu, “Investigation of Engine Fault Diagnosis Using Discrete Wavelet Transform and Neural Network,” Expert Systems with Applications, vol. 35, no. 3, pp. 1200-1213, 2008. [36] R. Rao, “Wavelet Transforms,” in Encyclopedia of Imaging Science and Technology, Hoboken, NJ, USA: Wiley, 2002. [37] Y. Souissi, M. A. Elaabid, N. Debande, S. Guilley, and J. L. Danger, “Novel Applications of Wavelet Transforms Based Side-channel Analysis,” In Non-invasive Attack Testing Workshop, Nara, JP, 2011. [38] 吳鴻謙、史天元、劉榮寬，〈以小波理論進行影像壓縮之三種實現比較〉，航測及遙測學刊，第10卷，第3期，頁305-314，2005年。 [39] C. S. Burrus, R. A. Gopinath, H. Guo, Introduction to Wavelets and Wavelet Transforms: A Primer, Hoboken, NJ, USA: Prentice Hall, 1997. [40] D. L. Donoho, and J. M. Johnstone, “Ideal Spatial Adaptation by Wavelet Shrinkage,” Biometrika, vol. 81, no. 3, pp. 425-455, 1994. [41] S. G. Mallat, “Denoising,” in A Wavelet Tour of Signal Processing: The Sparse Way, 3rd ed. Cambridge, MA, USA: Elsevier Academic Press, 2009, ch. 11, pp. 535-606. [42] B. Chen, et al., “Fault Feature Extraction of Gearbox by Using Overcomplete Rational Dilation Discrete Wavelet Transform on Signals Measured from Vibration Sensors,” Mechanical Systems and Signal Processing, vol. 33, pp. 275-298, 2012. [43] G. E. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time Series Analysis: Forecasting and Control, Englewood Cliffs, NJ: Wiley, 2015. [44] 张吉先、钟秋海、戴亚平，〈小波门限消噪法应用中分解层数及阈值的确定〉，中国电机工程学报，第24卷，第2期，頁118-122，2014年。 [45] S. Kim, D. An, and J. H. Choi, “Diagnostics 101: A Tutorial for Fault Diagnostics of Rolling Element Bearing Using Envelope Analysis in MATLAB,” Applied Sciences., vol. 10, no. 20, pp. 7302, 2020. [46] M. Srivastava, C. L. Anderson, and J. H. Freed, “A New Wavelet Denoising Method for Selecting Decomposition Levels and Noise Thresholds,” IEEE Access, vol. 4, pp. 3862-3877, 2016. [47] R. Liu, B. Yang, E. Zio, and X. Chen, “Artificial Intelligence for Fault Diagnosis of Rotating Machinery: A Review,” Mechanical Systems and Signal Processing, vol. 108, pp. 33-47, 2018. [48] Y. X. Liao, X. Q. Zeng, and W. H. Li, “Wavelet Transform Based Convolutional Neural Network for Gearbox Fault Classification,” in 2017 Prognostics and System Health Management Conference (PHM-Harbin), CN, pp. 1-6. [49] J. Bouvrie, “Notes on Convolutional Neural Networks,”, Cogprints, Nov. 2006. [50] D. P. Kingma, and J. Ba, “Adam: A Method for Stochastic Optimization,” arXiv: 1412.6980, Dec. 2014. [51] R. Huang, W. Li, and L. Cui, “An Intelligent Compound Fault Diagnosis Method Using One-dimensional Deep Convolutional Neural Network with Multi-label Classifier,” In 2019 IEEE International Instrumentation and Measurement. Technology Conference, pp. 1-6. [52] P. Liang, et al., “Compound Fault Diagnosis of Gearboxes via Multi-label Convolutional Neural Network and Wavelet Transform,” Computers in Industry, vol. 113, pp. 103132, 2019. [53] J. Shen, S. Li, F. Jia, H. Zuo,and J. Ma, “A Deep Multi-label Learning Framework for the Intelligent Fault Diagnosis of Machines,” IEEE Access, vol. 8, pp. 113557-113566, 2020. [54] Y. Yoo, and J. G. Baek, “A Novel Image Feature for the Remaining Useful Lifetime Prediction of Bearings Based on Continuous Wavelet Transform and Convolutional Neural Network,” Applied Sciences., vol. 8, no. 7, pp. 1102, 2018. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81782 | - |
| dc.description.abstract | " 離岸風力發電近年來逐漸發展為綠能產業中重要的一環，其中變速齒輪箱在整個風力發電機系統中是為最關鍵的組件之一。因風機在長時間運轉下維修不易，變速齒輪箱中的零件一旦損壞，除了維修成本巨大，嚴重甚至可能造成整台風機的崩潰。為了降低維護成本，狀態監測(condition monitoring)與故障診斷的技術也成為當今重要課題。 本文主要目的在於研究風力發電機的故障分類及診斷，並提出下列方法：(a)小波分析(wavelet analysis)，一種可以得知訊號在不同時間段頻率資訊的時頻分析，以及(b)卷積神經網路(convolution neural network, CNN)，一種常應用於影像分類的神經網路(neural network)，並應用上述兩種理論作為故障診斷的方法，利用振動訊號，針對模擬風機傳動系統的振動測試平台進行實驗。為模擬風機變轉速的情形，實驗僅訓練少數特定轉速，以其他中間轉速進行測試，並分別模擬齒輪箱在健康、齒輪故障、軸承故障和複合故障四種不同模式下的狀態。結果表明，本實驗在振動測試平台5-15 RPM的控制轉速下，能成功判斷出變轉速中四種不同的模式，準確率最高可達到97.91%，可靠度則為97.78%。 " | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T03:03:29Z (GMT). No. of bitstreams: 1 U0001-2008202115054900.pdf: 5225946 bytes, checksum: fa3bd3f2fd0720e8e05ba1c3c0d72cf6 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員會審定書 # 誌謝 i 摘要 ii ABSTRACT iii 目錄 iv 圖目錄 vi 表目錄 x 第一章 緒論 11 1.1 研究動機與目的 11 1.2 文獻回顧 12 1.2.1 訊號量測方法 13 1.2.2 基於振動訊號的故障特徵提取 13 1.2.3 分類及診斷方法 16 1.3 論文架構與研究方法 17 第二章 背景理論 18 2.1 風機齒輪箱故障類型及振動特徵 18 2.1.1 風機齒輪箱結構和特性 18 2.1.2 風機齒輪箱主要故障類型 19 2.1.3 齒輪振動特徵頻率 19 2.1.4 軸承振動特徵頻率 21 2.2 小波分析 23 2.2.1 連續小波轉換 24 2.2.2 離散小波轉換 27 2.2.3 小波分解及去雜訊 30 2.3 故障分類及診斷方法 39 2.3.1 人工神經網路 39 2.3.2 卷積神經網路 41 2.3.3 神經網路的訓練 43 2.3.4 損失函數(Loss Function)及參數最佳化(Optimization) 44 第三章 實驗設備及訊號擷取 48 3.1 實驗儀器與設備 48 3.2 實驗架設與訊號擷取 51 第四章 振動特徵分析與訊號處理 58 4.1 振動特徵分析 58 4.2 訊號處理 61 第五章 卷積神經網路故障分類及診斷 80 5.1 資料集的劃分 80 5.2 CNN架構與參數 82 5.3 CNN故障分類及診斷改進 88 5.4 分類及診斷結果與討論 90 第六章 結論與未來展望 92 6.1 結論 92 6.2 未來展望 92 參考文獻 94 | |
| dc.language.iso | zh-TW | |
| dc.subject | 神經網路 | zh_TW |
| dc.subject | 故障分類及診斷 | zh_TW |
| dc.subject | 變速齒輪箱 | zh_TW |
| dc.subject | 小波分析 | zh_TW |
| dc.subject | 風力發電機 | zh_TW |
| dc.subject | wavelet analysis | en |
| dc.subject | fault classification and diagnosis | en |
| dc.subject | neural network | en |
| dc.subject | gearbox | en |
| dc.subject | wind turbine | en |
| dc.title | 小波分析與卷積神經網路於風機齒輪箱振動故障診斷之應用 | zh_TW |
| dc.title | Vibration-based Fault Diagnosis of Wind Turbine Gearbox Using Wavelet Analysis and Convolution Neural Network | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 蔡進發(Hsin-Tsai Liu),林益煌(Chih-Yang Tseng),黃心豪 | |
| dc.subject.keyword | 風力發電機,變速齒輪箱,小波分析,神經網路,故障分類及診斷, | zh_TW |
| dc.subject.keyword | wind turbine,gearbox,wavelet analysis,neural network,fault classification and diagnosis, | en |
| dc.relation.page | 98 | |
| dc.identifier.doi | 10.6342/NTU202102551 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-08-23 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
| dc.date.embargo-lift | 2023-08-01 | - |
| 顯示於系所單位： | 工程科學及海洋工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-2008202115054900.pdf | 5.1 MB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。