第五章 結論及未來展望
5.2 未來展望
藉由第四章最後實驗結論的部分，本論文提到了，實驗所使用的方法，
是針對 1774 至 2121 RPM 的轉速範圍建立的決策樹建立一辨識決策樹，在 取用其他轉速範圍當作測試資料時，分類正確率便下降許多，因此需要針對 不同的轉速範圍建立不同的決策樹，所以若欲對收集到的所有實驗資料完成 診斷，勢必需要將共 141 種轉速依照特性分為若干個轉速範圍，並對所有的 轉速範圍建立各自之決策樹，架構一個完全轉速範圍的分類決策樹。
Morlet 轉換、解調變頻譜分析、多尺度熵以及多尺度頻譜熵，在齒輪箱 結構的特徵抽取上，可以抽取出有助於辨識的有效特徵，但是由於時頻分析 演算法所需要的時間會隨著實驗資料長度的變換而使得計算時間改變，而多 尺度頻譜熵演算法的效果也會受樣本長度的影響，這都是可以改善的地方。
本論文之實驗方法尚為針對本齒輪箱資料所建立，故未能堪稱完整，未 來希望若可取得其他的齒輪箱資料，使用本論文的方法建立他種的齒輪箱診 斷決策樹，以期完成目標之齒輪箱異常診斷系統。
61
參考文獻
[1] Z. Xu, J. Xuan, T. Shi, B. Wu, and Y. Wu, “A Novel Fault Diagnosis Method of Bearing Based on Improved Fuzzy ARTMAP and Modified Distance Discriminant Technique,” Expert System with Applications, vol. 36, pp.
11801-11807, 2009.
[2] S.Wadhwani, A. K. Wadhwani, S. P. Gupta and V. Kumar, “Detection of Bearing Failure in Rotating Machine Using Adaptive Neuro-Fuzzy Inference System,” in Power Electronics, Drives and Energy Systems, 2006.
PEDES ’06. International Conference on, New Delhi, 2006.
[3] L. Choen, “Time-frequency distributions-a review,” Proceeding of the IEEE, vol. 77, no. 7, 1989.
[4] H. Oehlmann, D. Brie, M. Tomczak and A. Richard, “A Method for Analysing Gearbox Faults Using Time-Frequency Representations,”
Mechanical Systems and Signal Processing, vol. 11, pp. 529-545, 1997.
[5] W. J. Staszewski, K. Worden and G. R. Tomlinson, “Time-Frequency Analysis in Gearbox Fault Detection Using the Wigner-Ville Distribution and Pattern Recongnition,” Mechanical Systems and Signal Processing, vol.
11, pp. 673-692, 1997.
[6] D. Gabor, “Theory of Communications,” Journal of the Institution of Electrical Engineers, vol. 93, no. 4, pp. 429-457, 1946.
[7] F. Al-Badourl, L. Chedect, and M.Suna, “Non-Stationary Vibration Signal Analysis of Rotating Machinery via Time-Frequency and Wavelet Techniques,” Information Sciences Signal Processing and their Applications, pp. 21-24, 2010.
62
[8] J. Lin and M. J. Zuo, “Gearbox Fault Diagnosis Using Adaptive Wavelet Filter,” Mechanical Systems and Signal Processing, vol. 17, pp. 1259-1269, 2003.
[9] H. Zheng, Z. Li and X. Chen, “Gear Fault Diagnosis Based on Continuous Wavelet Transform,” Mechanical Systems and Signal Processing, vol. 16, pp.
447-457, 2002.
[10] J. Lin and A. Zhang, “Fault Feature Separation Using Wavelet-ICA Filter,”
NDT & E International, vol. 38, pp. 421-427, 2005.
[11] W. X. Yang and X. M. Ren, “Detecting Impulses in Mechanical Signals by Wavelets,” EURASIP Journal on Applied Signal Processing, vol. 2004, pp.
1156-1162, 2004.
[12] D. Boulahbal, M. F. Golnaraghi and F. Ismail, “Amplitude and phase wavelet maps for the detection of cracks in geared systems,” Mechanical Systems and Signal Processing, vol. 13, pp. 423-436, 1999.
[13] W. J. Wang and P. D. McFadden, “Application of Wavelets to Gear Box Vibration Signals for Fault Detection,” Journal of Sound and Vibration, vol.
192, no. 5, pp. 927-939, 1996.
[14] J. Lin and L. Qu, “Feature Extraction Based on Morlet Wavelet and Its Application for Mechanicla Gault Diagnosis,” Journal of Sound and Vibration, vol. 234, pp. 135-148, 2000.
[15] B. D. Forrester, “Use of the Wigner-Ville Distribution in Helicopter Transmission Fault Detection,” Proceedings of the Australian Symposium on Signal Processing and Applications, 1989.
[16] B. D. Forrester, “Analysis of Gear Vibration in the Time-Frequency Domain,”
Proceedings of the 44th Meeting of the Mechanical Failures Prevention
63
Group of the Vibration Institute, 1989.
[17] B. D. Forrester, “Time–Frequency Analysis of Helicopter Transmission Vibration,” Aeronautical Research Laboratory, ARL Propulsion, Melbourne, Technical Report 180, 1990.
[18] B. D. Forrester, “Time–Frequency Analysis in Machine Fault Detection,”
Time–Frequency Signal Analysis, 1992.
[19] W. J. Staszewski, “The Application of Time-Variant Analysis to Gearbox Fault Detection,” Manchester School of Engineering, Mechanical Engineering Department, the University of Manchester, 1994.
[20] Y. N. Pen, J. Chen and X. L. Li, “Spectral Entropy: A Complementary Index for Rolling Element Bearing Performance degradation Assessment,” Proc.
IMechE Part C: J. Mechanical Engineering Science, no. 223, pp. 1223-1231, 2009.
[21] J. Hu, R. Shao and Z. Zeng, ”Method of Gear's Fault Diagnosis based on Spectral Entropy,” Journal of Mechanical Transmission, May 2007.
[22] B. Li, P. Zhang, S. Liang and G. Ren, “Feature Extraction and Selection for Fault Diagnosis of Gear Using Wavelet Entropy and Mutual Information,” in International Conference on Signal Processing, Beijin, China, 2008.
[23] L. Zong, G. Xiong, H. Liu, H. Zou and W. Guo, “Bearing Fault Diagnosis Using Multi-scale Entropy and Adaptive Neuro-Fuzzy Inference,” Expert System with Applications, vol. 37, pp. 6077-6085, 2010.
[24] J. L. Lin, J. Y. Liu, C. W. Li, L. F. Tsai and H. Y. Chung, “Motor Shaft Misalignment Detection Using Multiscale Entropy with Wavelet Denoising,”
Expert System with Applications, vol. 37, pp. 7200-7204, 2010.
[25] 王俊傑、吳求文、吳順德、李易宗、吳豐泰,“多尺度頻譜熵在軸承異常
64
監控與診斷之應用,”於 第二十八屆中國機械工程學術研討會, 台中市, 2011.
[26] R. Yan and R.X. Gao, ”Approximate Entropy as a Diagnosis Tool for Machine Health Monitoring,” Mechanical Systems and Signal Processing, vol. 21, pp. 824-839, 2007.
[27] R. Yan and R.X. Gao, ”Complexity as a Measure for Machine Health Evalution,” IEEE Transaction on, vol. 53, no. 4, pp. 1327-1334, 2004.
[28] H. Hong and M. Liang, “Fault Severity Assessment for Rolling Element Bearing Using the Lempel-Ziv Complexity and Continuous Wavelet Transform,” Journal of Sound and Vibration, vol. 320, pp. 452-468, 2009.
[29] J. Huang, H. Pen and S. Bi, “Bispectrum Entropy Feature Extraction and Its Application for Fault Diagnosis of Gearbox,“ in Fussy Systems (FUZZ), 2010 IEEE International Conference on, Barcelona, 2010.
[30] R. Hao, Z. Peng, Z. Feng and F. Chu, “Application of Support Vector Machine Based on Pattern Spectrum Entropy in Fault Diagnostics of Rolling Element Bearings,” Measurement Science and Technology, vol. 22, p.
045708, 2011.
[31] D. Yu, Y. Yang and J. Cheng, “Application of Time Frequency Entropy Method Based on Hilbert Huang Transform to Gear Fault Diagnosis,”
Measurement, vol. 40, pp. 823-830, 2007.
[32] 林庭毅、吳順德、林倪鋒,“基於多尺度方均根植與前饋式倒傳遞網路隻 機械錯誤診斷系統,”於 2012 中華民國系統科學與工程延討論會, 基隆 市, 2012.
[33] P. D. McFadden, “Detection of Gear Faults by Decomposition of Matched Differences of Vibration Signals,” Mechanical Systems and Signal
65
Processing, vol. 14, pp. 805-817, 2000.
[34] CRYSTAL instruments, http://www.go-ci.com/
[35] B. Boashasj, Time-Frequency Signal Analysis and Processing: A Comprehensive Reference, Australia, 2003.
[36] 褚福磊，彭志科，馮志鵬，李志农，機械故障診断中的現代信號處理方 法，第一版，科學出版社，北京，2009 年 10 月.
[37] P. S. Addison, The Illustrated Wavelet Transform Handbook, Institute of Physics, 2002.
[38] A. Akansu and R. Haddad, Multiresolution Signal Decomposition:
Transforms, Subbands, Wavelets, Academic Press, 1992.
[39] S. Mallat, A wavelet tour of signal processing, 2nd Edition, Academic Press, 1999.
[40] A. Bernardino and J. Santos, “A Real-time Gabor Primal Sketch for Visual Attention,” 2nd Iberian Conference on Pattern Recognition and Image Analysis, 2005.
[41] P. Goupillaud, A. Grossman and J. Morlet, Cycle-Octave and Related Transforms in Seismic Signal Analysis, Geoexploration, 1984.
[42] M. T. Taner, “Joint Time/Frequency Analysis, Q Quality factor and Dispersion computation using Gabor-Morlet wavelets or Gabor-Morlet transform,” Rock Solid Images, 1983.
[43] Y. N. Jeng, C. T. Chen and Y. C. Cheng, “The Enhanced Morlet Transform via Iterative Filter to Study Turbulent Data Strings,” The 6th Aslan Computational Fluid Dynamics Conference Taiwan, 2005.
[44] 鄭育能、陳季聰、鄭又齊,“觀察一維紊流數據的一種有效之新拆解法,”
第十二屆全國計算流體力學學術研討論會, 2005.
66
[45] M. Costa, A. L. Goldberger amd C. K. Peng, “Multiscale Entropy Analysis of Complex Physiological Time Series,” Physical Review Letters, vol. 9, no.
6, pp. 068102-1-068102-4, 2002.
[46] M. Costa, C. K. Peng, A. L. Goldberger and J. M. Hausdorff, “Multiscale Entropy Analysis of Human Gait Dynamics,” Physica A: Statistical Mechanics and its Applications, vol. 330, pp. 53-60, 2003.
[47] J. S. Richman and J. R. Moorman, “Physiological Time-Series Analysis Using Approximate Entropy and Sample Entropy,” American Journal of Physiology Heart and Circulatory Physiology, vol. 278, no. 6, pp.
2039-2049, 2000.
[48] 國 立 臺 灣 師 範 大 學 訊 號 處 理 實 驗 室 http://spl.mt.ntnu.edu.tw/ITRI_data_menu.html
[49] T. S. Irwin, Gearbox Spectral Components and Monitoring Methods.
[50] 傅继盈，蒋秀珍，機械學基礎，第三版，哈爾濱工業大學出版社，2003 年 8 月.
[51] Y. G. Lei, M. J. Zuo, Z. J. He and Y. Y. Zi, “A Multidimensional Hybrid Intelligent Method for Gear Fault Diagnosis,” Expert Systems With Applications, vol. 37, pp. 1419-1430, 2010.
[52] Z. X. Li, X. P. Yan, C. Q. Yuan, Z. X. Peng and L. Li, “Virtual Prototype and Experimental Research on Gear Multi-Fault Diagnosis Using Wavelet-Autoregressive Model and Principal Component Analysis Method,”
Mechanical System and Signal Processing, no. 25, pp. 2589-2607, 2011.
[53] W. X. Sun, J. Chen and J. Q. Li, “Decision tree and PCA-based Fault Diagnosis of Rotating Machinery,” Mechanical System and Signal Processing, vol. 21, pp. 1300-1317, 2007.
67
[54] W. C. Kao, C. K. Yu, C. P. Shen and P. Y. Hsiao, “Electrocardiogram Analysis with Adaptive Feature Seletion and Support Vector Machines,“ in Proc. IEEE Asia Pacific Conference on Circuits and Systems, Singapore, 2006.
[55] C. W. Hsu and C.J. Lin, “A Comparison of Method for Multiclass Support Vector Machines,” IEEE Transactions on Neural Network, vol. 13, no. 2, pp.
415-425, 2002.
[56] W. Sun, J. Chen, J. Li, “Decision tree and PCA-based fault diagnosis of rotating machinery,” Mechanical Systems and Signal Processing, vol. 21, pp.
1300-1317, 2006.