臺灣博碩士論文加值系統

(44.192.79.149) 您好！臺灣時間：2023/06/10 02:25

:::

詳目顯示

:

• 被引用:0
• 點閱:170
• 評分:
• 下載:0
• 書目收藏:0
 本文針對電腦風扇葉片缺損的振動特徵，利用希爾伯特-黃轉換 (Hilbert-Huang Transform, HHT)進行分析與檢測。HHT是一種適用於非平穩與非線性訊號的時頻分析工具，其分析流程首先將待分析訊號用經驗模態分解法(Empirical Mode Decomposition,EMD)分解出數個本質模態函數 (Intrinsic Mode Functions, IMFs)。其次再利用Hilbert轉換(Hilbert Transform, HT)求得該待測訊號之瞬時頻率、瞬時振福，進而建立兼具時-頻-能量三者的分佈圖，稱為希爾伯特時頻譜(Hilbert Spectrum)。文中利用此數據，將風扇旋轉頻率上之平均能量除以振動總能量定義為損傷程度，以此作為破壞檢測指標之一。此外並比較不同破壞程度之希爾伯特時頻譜與偏度之歐式距離當作另一個破壞檢測指標。實驗部分將風扇分成無損壞、輕微損壞及嚴重損壞三種情況，分別量測並分析其希爾伯特時頻譜，再計算其診斷指標，用以判斷其破壞程度，依據分析結果：以平均能量除以振動總能量為診斷指標其準確度為87.5%；以不同破壞程度之希爾伯特時頻譜與偏度之歐式距離為診斷指標，轉速為3100(RPM)之風扇其準確度為85.42%，轉速為2500(RPM)之風扇其準確度為58.33%，轉速為2000(RPM)之風扇其準確度為60.42%。
 The current study investigates the property of vibration on the damaged cooling fans via Hilbert-Huang Transform (HHT) method. HHT is a time-frequency analysis tool commonly used to test the nonstationary and nonlinear signals. HHT consists of two procedures when applied to the analysis: (a) The utilization of Empirical Mode Decomposition (EMD) to extract Intrinsic Mode Functions (IMFs) from signals to be processed. (b) The utilization of Hilbert Transform (HT) to obtain Instantaneous frequency and Instantaneous amplitude from signals to be processed. Base on data, the degree of damage is defined as the average energy of fan rotation frequency divided by total energy of vibration. The degree of damage, thus, is used as the indicator for damage detecting. Being classified into undamaged, slightly damaged and seriously damaged, the cooling fans with different classification are compared via Hilbert spectrum to fulfill the damage detecting in the current study. According to the analysis, by considering the result of average energy of fan rotation frequency divided by total energy of vibration as the indicator for damage detecting, the accuracy is 87.5%. With the indicator for damage detecting defined as the distance between Hilbert spectrum of different classification of cooling fans and skewness, the accuracy is 85.42% when the rotation speed of cooling fan is 3100(RPM), 58.33% when the rotation speed of cooling fan is 2500(RPM), and 60.42% when the rotation speed of cooling fan is 2000(RPM).
 摘要 IAbstract II目錄 III圖目錄 V表目錄 VII 第一章 緒論 11.1 研究動機 11.2 文獻回顧 21.3 研究內容及大綱 5 第二章 HHT基礎理論 62.1 瞬時頻率與解析訊號 62.2 希爾伯特黃轉換(Hilbert-Huang Transform, HHT) 132.2.1 本質模態函數(Intrinsic Mode Functions, IMF) 132.2.2 經驗模態分解法(Empirical Mode Decomposition, EMD) 142.2.3 希伯特頻譜 182.3 總體經驗模態分解法(Ensemble EMD,EEMD) 182.4 遮罩訊號法(MASK SIGNAL) 202.5 希爾伯特黃轉換之特性 21 第三章損壞診斷指標 233.1 旋轉頻率之能量比值(指標一) 233.2 相似度(指標二) 243.2.1 頻譜之歐式距離 243.2.2 偏度(skewness)之歐式距離 25 第四章 實驗設備與系統架構 264.1 實驗設備與流程 264.2 訊號量測儀器 274.3 訊號量測過程 29 第五章 實驗設計與結果分析 325.1 實驗設計 325.2 各轉速風扇與其指標 335.2.1 轉速3100(RPM)風扇 335.2.2 轉速2000(RPM)風扇 375.2.3 轉速2000(RPM)風扇 405.3 以各特徵設立診斷指標 445.3.1 以c值為特徵設立診斷指標 445.3.2 以頻譜相似度為特徵設立診斷指標 46 第六章 結論及未來展望 516.1 結論 516.2 未來展望 52參考文獻 53
 [1]X. Tian , Hewlett-Packard Co., "Cooling Fan Reliability: Failure Criteria, Accelerated Life Testing, Modeling and Qualification." ,Reliability and Maintainability Symposium, pp.380-384. , 2006.[2]C. W. Lin, "希爾伯特黃轉換於樑上之破壞檢測分析", 台灣大學工程科學及海洋工程學系, 碩士論文, 2010.[3]Z Hameed, Y. S Hong, Y. M Cho, S. H Ahn ,C. K Song, "Condition monitoring and fault detection of wind turbines and related algorithms: A review. " ,Vol.13, Issue 1, pp.1-39. , 2009.[4]B. K. N RAO, "Handbook of Condition Monitoring. ", Elsevier Science ,1996.[5]C. L. Lee, "音射定位法於岩石材料之應用", 國立成功大學資源工程學系, 碩士論文, 2003.[6]L. Lin, W. Lu, F. Chu, "Application of AE techniques for the detection of wind turbine using Hilbert-Huang transform.", Prognostics and Health Management Conference, pp.1-7.,2010.[7]A. G. Dutton, "Thermoelastic stress measurement and acoustic emission monitoring in wind turbine blade testing." ,European Wind Energy Conference, pp.22-25., 2004.[8]F. Hahn, C. W. Kensche, R. J. H. Paynter, A. G. Dutton, C. Kildegaard and J. Kosgaard, " Design, Fatigue Test and NDE of a Sectional Wind Turbine Rotor Blade. Journal of Thermoplastic Composite Materials ",Vol.15, no. 3 ,pp.267-277., 2002.[9]N. Roy, R. Ganguli, " Helicopter rotor blade frequency evolution with damage growth and signal processing. ", Journal of Sound and Vibration , Vol.283 , Issue 3-5 ,pp. 821-851. 2005.[10]S. Kumar, N. Roy, R. Ganguli, " Monitoring low cycle fatigue damage in turbine blade using vibration characteristics. ",Mechanical Systems and Signal Processing , Vol. 21, Issue 1, pp. 480–501., 2007.[11]J. T. Sawicki, X. Wu, G. Y. Baaklini, , A. L. Gyekenyesi, "Vibration-based crack diagnosis in rotating shafts during acceleration through resonance. " Proceedings of SPIE,Vol. 5046, Issue 1, 2003.[12]T. R. Babu, S. Srikanth, A.S. Sekhar, "Hilbert–Huang transform for detection and monitoring of crack in a transient rotor." ,Mechanical Systems and Signal Processing ,Vol.22, Issue 4,pp.905–914,2008.[13]Q. Miao, M. Azarian, M. Pecht, " Cooling Fan Bearing Fault Identification Using Vibration Measurement. " ,Prognostics and Health Management (PHM),pp.1-5.,2011[14]C. K. Cheng, P. H. Chen , A. Liu , L. M. Chen, "Defect Type Recognition System for Wind Turbine by Subtractive Clustering. " ,Intelligent System Design and Engineering Application (ISDEA),pp.1404 - 1408.,2012.[15]J. W. Cooley, J. W. Turkey, "An Algorithm for the Machine Calculation of Complex Fourier Series ", Mathematics of Computation, Vol. 19, Issue 90, pp. 297-201., 1965[16]S. Qian, D. Chen, "Joint time-frequency analysis : methods and applications", Prentice Hall, 1996[17]Y. Meyer, "Wavelets : algorithms & applications ", Philadelphia : Society for Industrial and Applied Mathematics, 1993[18]N. E. Huang, S. S. Shen, "The Hilbert-huang Transform And Its Applications", World Scientific Pub Co. Pte. Ltd., 2005[20]K. Y. Chen , H. C. Yeh, S. Y. Su, C. H. Liu, N. E. Huang, "Anatomy of plasma structures in an equatorial spread F event. " , Geophysical Research Letters, Vol. 28, Issue 16, pp. 3107-3110., 2001[21]R.W. Komm, F. Hill, R. Howe, " Empirical mode decomposition and Hilbert analysis applied to rotation residuals of the solar convection zone. " , The Astrophysical Journal, Vol.558, Issue 1, pp 428-441., 2001[22]N. E. Huang, Z. Shen , S. R. Lomg , M. C. Wu , S. H. Shih, Q. Zheng, C. C. Tung, H. H. Liu, "The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis. " ,Proceedings of the Royal Society A, Vol.454, No.1971, pp 903-995, 1998[23]L. Cohen, "Time-Frequency Analysis.", Prentice Hall PTR, Englewood Cliffs, 1995[24]M. Schwartz , W. R. Bennett, S. Stein, "Communications Systems and Techniques. ", New York, McGraw-Hill.,1966.[25]S. O. Rice, "Mathematical Analysis of Random Noise. ", Bell System Technical Journal, Vol. 23 , Issue 3 ,pp. 282-310, 1994[26]D. Gabor, " Communication Theory and Physics. ", IEEE Transactions on Information Theory, Vol. 1, No. 1, pp. 48-59, 1953[27]E. Bedrosian, "A Product Theorem for Hilbert Transforms. ", Proceedings of the IEEE, Vol.51, No. 5, pp. 868-869., 1963.[28]N. E. Huang, Z. Shen, R. S. Long, "A new view of nonlinear water waves – the Hilbert spectrum. ", Annual Review of Fluid Mechanics, Vol.31, pp. 417-457., 1999.[29]N. E. Huang, M. L. Wu, S. R. Long, S. S. Shen, W. D. Qu, P. Gloersen, K. L. Fan, "A confidence limit for the empirical mode decomposition and the Hilbert spectral analysis. " , Proceedings of the Royal Society, London, Vol. 459A, pp.2317-2345, 2003[30]Z. Wu , N. E. Huang, " Ensemble empirical mode decomposition: A noise-assisted data analysis method.", Advances in Adaptive Data Analysis, Vol. 1, Issue 1, pp. 1-41, 2009[31]R. Deering, J. F. Kaiser, "The use of a masking signal to improve empirical mode decomposition." , Acoustics Speech and Signal Processing, Proceedings. (ICASSP ''05). IEEE International, Vol. 4, Issue 4, pp.485-488., 2005.[32]N. Senroy, S. Suryanarayanan, "Two Techniques to Enhance Empirical Mode Decomposition for Power Quality Applications. ", Power Engineering Society General Meeting, 2007. IEEE, pp.1-6., 2007.[33]N. Senroy, S. Suryanarayanan, P. F. Ribeiro, "An Improved Hilbert–Huang Method for Analysis of Time-Varying Waveforms in Power Quality.", IEEE Transactions on Power Systems, Vol. 22, pp. 1843-1850, 2007.[34]H. Zhang, Q. Gai, "Research on Properties of Empirical Mode Decomposition Method. " , Intelligent Control and Automation( WCICA 2006.) The Sixth World Congress, Vol.2, pp.10001-10004, 2006.
 國圖紙本論文
 推文當script無法執行時可按︰推文 網路書籤當script無法執行時可按︰網路書籤 推薦當script無法執行時可按︰推薦 評分當script無法執行時可按︰評分 引用網址當script無法執行時可按︰引用網址 轉寄當script無法執行時可按︰轉寄

 1 應用希爾伯特黃變換(HHT)之邊際譜分析於旋轉機械的元件鬆脫故障診斷 2 總體經驗模態分解法(EEMD)結合自回歸(AR)模型在旋轉機械之元件鬆脫故障診斷之應用 3 運用多尺度熵及希伯特頻譜分析於穴位磁療法提升老年人平衡力之研究 4 脈搏訊號之時頻域分析 5 經驗模態分解應用於敲擊回音法之鋼筋與裂縫辨識 6 探討扁平足與正常足者之平衡差異性研究 7 EMD訊號擷取模式及HHT頻譜解析圖形判讀準則建立與混凝土橋損傷驗證 8 經驗模態分解法於渦輪幫浦軸承故障診斷研究 9 經驗模態分解法於敲擊回音法之應用 10 藉由長者專用健康鞋刺激平衡力以預防跌倒之研究 11 希爾伯特黃轉換於心電訊號之分析與模擬 12 應用希爾伯特-黃轉換於GPS訊號抗干擾之研究 13 誤差自相關情況下的無母數迴歸分析 14 經驗模態分解法的本質模態函數新搜尋條件 15 開發具有反覆學習控制演算法之高精度CNC運動控制器

 1 鍾全雄, 黃國芳, 王博賢, 游鎮烽. (2007). 高精密無機質譜儀在地球科學及海洋科學研究上的應用. 中國化學會期刊, 第65卷第二期, 頁113~124.

 1 小波轉換於風力發電機葉片診斷之應用 2 多目標基因演算法於揚聲器腔體最佳化之研究 3 動圈式揚聲器非線性失真之控制研究 4 動圈式揚聲器單體非線性參數之估測法 5 音箱設計對揚聲器暫態響應之影響分析 6 振膜材質組合對微型揚聲器輸出影響探討 7 希爾伯特黃轉換於樑上之破壞檢測分析 8 揚聲器初步設計所需之參數研究 9 翼尖小翼對風車葉片流場及空氣動力噪音之影響探討 10 氣體流經槽孔型消音器產生之氣動力噪音分析 11 快速多極展開法於消音器性能分析之應用 12 不同氣動噪音預測方法應用於風車葉片之比較研究 13 短時傅立葉轉換於風力發電機葉片表層損傷即時診斷之應用 14 人工植體頸部型態的差異對周圍骨質應變之影響 15 以多頻道聽覺穩定刺激反應預測早產兒之聽力閥值

 簡易查詢 | 進階查詢 | 熱門排行 | 我的研究室