本論文提出熔融紡絲機台之加工參數異常診斷，採用聚丙烯材料，利用田實驗規劃法進行實驗與設計，而加工參數包含壓出機加熱溫度曲線、螺桿轉速、齒輪幫浦轉速、冷卻風速與捲取速度五個因子，搭配張力感測器即時量測紡絲張力變異，計算訊號雜音比、變異數分析與信賴區間，根據確認實驗選擇最佳參數組合，以最佳參數組合為標準，單一顯著加工參數的改變進行實驗。將實驗結果，利用小波包變換擷取訊號的資訊，並針對這些子訊號計算熵和計算原始訊號的標準差值作為特徵向量。應用三層倒傳遞類神經網路做為分類器，將異常的特徵向量作為此分類器的輸入，並進行訓練。由實驗結果可以看出，所提出的方法可以有效及正確的區別三種異常加工參數，壓出機加熱溫度曲線、冷卻風速與捲取速度，並可以建立一套自動檢測加工參數異常的辨識系統。

This thesis proposes melt spinning fault diagnosis, which can diagnose something wrong with process parameters. We use the material of polypropylene (PP) in experiments and the Taguchi method to plan the experiment. Five process parameters include the heating temperature’s curve of the extruder, formation speed, metering pump speed, cooling air speed and take-up velocity. The variance of spinline tension is measured by a tension sensor and the results are used to calculate such data as signal-to-noise ratio, analysis of variance and confidence interval. The significant factors and the best combination of process parameters are found by the confirmation experiment. The experiments are conducted based on the change of a single process parameter. The wavelet packet transform of the tension signal is obtained, and we compute the feature values of entropy from the sub-signals and standard deviation from original signal. A three-layer neural network with back-propagation algorithm is trained to be a classifier. The experiment results show the faults in three signification factors, heating temperature’s curve of the extrude, cooling air speed and take-up velocity, can be effectively and precisely diagnosed.

1吳偉欽，“聚丙烯纖維染色性之探討”，絲織園地，第26期。
2A. Dutta and V. M. Nadkarni, “Identifying Critical Process Variable in Poly(ethylene terephthalate) Melt Spinning,” Textile Research Journal, Vol. 54, No. 1, pp. 35-42 (1984).
3C. Jinan, T. Kikutani, A. Takaku and J. Shimizu, “Nonisothermal Orientation-induced Crystallization in Melt Spinning of Polypropylene,” Journal of Applied Polymer Science, Vol. 37, pp. 2686-2697 (1989).
4J. S. Denton, J. A. Cuculo and P. A. Tucker, “Computer Simulation of High-speed Spinning of PET,” Journal of Applied Polymer Science, Vol. 57, pp. 939-951 (1995).
5S. Chen, W. Yu and J. E. Spruiell, “On-line Studies of Structure Development during Melt Spinning of Poly(butylenes telephthalate),” Journal of Applied Polymer Science, Vol. 34, pp. 1477-1492 (1987).
6K. F. Zieminski and J. Spruiell, “On-line Studies and Computer Simulation of the Melt Spinning of Nylon-66 Filaments,” Journal of Applied Polymer Science, Vol. 35, pp.2223-2245 (1988).
7Y. C. Bhuvanesh and V. B. Gupta, “Computer Simulation of Melt Spinning of Polypropylene Fibers Using a Steady-State Model,” Journal of Applied Polymer Science, Vol. 58, pp. 663-674 (1995).
8鍾清章 校訂，田口式品質工程導論，中華民國品質管制學會發行 (1989)。
9R. S. Chen, H. H. Lee and C. Y. Yu, “Application of Taguchi Method on the Optimal Process Design of an Injection Model PC/PBT Automobile Bumper,” Composite Structures, Vol. 39, No. 3-4, pp. 209-214 (1997).
10Y. S. Zu and S. T. Lin, “Optimizing the Mechanical Properties of Injection Molded W - 4.9% Ni - 2.1% Fe in Debinding,” Journal of Materials Processing Technology, Vol. 71, pp. 337-342 (1997).
11C. Chen, P. Jen and F. S. Lai, “Optimization of the Coathanger Manifold Via Computer Simulation and an Orthogonal Array,” Polymer Engineering and Science, Vol. 37, No. 1, pp. 188-196 (1997).
12S. J. Liu, C. C. Lai and S. T. Lin, “Optimizing the Impact Strength of Rotationally Molded Parts,” Polymer Engineering and Science, Vol. 40, No. 2, pp. 473-480 (2000).
13P. S. Wright, “Short-Time Fourier Transforms and Wigner-Ville Distributions Applied to the Calibration of Power Frequency Harmonic Analyzers,” IEEE Transactions on Instrumentation and Measurement, Vol. 48, No. 2, pp. 475-478 (1999).
14S. 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).
15A. Grossman and J. Morlet, “Decompositions of Hardy Functions into Square Integrable Wavelets of Constant Shape,” SIAM Journal of Mathematical Analysis, Vol. 15, No.4, pp. 723-736 (1984).
16J. Jin and J. Shi, “Automatic Feature Extraction of Waveform Signals for In-Process Diagnostic Performance Improvement,” Journal of Intelligent Manufacturing, Vol. 12, pp. 257-268 (2001).
17S. Santoso, E. J Powers, W. M. Grady and P. Hofmann, “Power Quality Assessment via Wavelet Transform Analysis,” IEEE Transactions on Power Delivery, Vol. 11, No. 2, pp. 924-930 (1996).
18A. Elmitwally, S. Farghal, M. Kandil, S. Abdelkader and M. Elkateb, “Proposed Wavelet-Neurofuzzy Combined System for Power Quality Violations Detection and Diagnosis,” IEE Proceedings: Generation, Transmission and Distribution, Vol. 148, No. 1, pp. 15-20 (2001).
19X. Lou and K. Loparo, “Bearing Fault Diagnosis Based on Wavelet Transform and Fuzzy Inference,” Mechanical Systems and Signal Processing, Vol. 18, No. 5, pp. 1077-1095 (2004).
20Y. H. Peng, X. G. Xu and H. X. Zhao, “Application of Wavelet Packet Analysis in Turbine Fault Diagnosis”, Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, pp. 13-16 (2006).
21D. S. S. Lee, B. J. Lithgow and R. E. Morrison, “New Fault Diagnosis of Circuit Breakers,” IEEE Transactions on Power Delivery Vol. 18, No. 2, pp. 454-459 (2003).
22Z. Wang, Y. Liu and P. J. Griffin, “Neural Net and Expert System Diagnose Transformer Faults,” IEEE Computer Applications in Power, Vol. 13, pp. 50-55 (2000).
23Y. Zhou, B. Zhao and D. Wu, “Application of Genetic Algorithms to Fault Diagnosis in Nuclear Power Plants,” Reliability Engineering and System Safety, Vol. 67, pp. 153-130 (2000).
24K. Shubhalaxmi, P. K. Chande and P. C. Sharma, “Automobile Engine Fault Diagnosis Using Neural Network,” IEEE Intelligent Transportation Systems Conference Proceedings, pp. 492-495 (2001).
25洪錫軍，李昱明，李以心，基於小波分析和FFT的紗線質量在線檢測與控制，上海交通大學學報，第4期 (2002)。
26S. Kase and T. Matsuo, “Studies on Melt Spinning. I. Fundamental Equation on the Dynamics of Melt Spinning,” Journal of Applied Polymer Science, Vol. 3, pp. 2541-2554 (1965).
27S. Kase and T. Matsuo, “Studies on Melt Spinning. II. Fundamental Equation on the Dynamics of Melt Spinning”, Journal of Applied Polymer Science, Vol. 11, pp. 251-287 (1967).
28A. Ziabicki, Fundamentals of Fiber Formation, John Wiley, London (1976).
29Z. Tadmor and C. G. Gogos, Principle of Polymer Processing, John Wiley, New York (1979).
30李輝煌，田口方法：品質設計的原理與實務，初版台北：高立 (2000)。
31黎正中，穩健設計之品質工程，台北：台北圖書 (1993)。
32吳復強，田口品質工程初版，台北：全威 (1992)。
33鍾清章，校訂，田口式品質工程導論，中華民國品質管制學會發行 (1989)。
34蘇朝墩，產品穩健設計，中華民國品質學會 (2002)。
35P. J. Ross, Taguchi Techniques for Quality Engineering, McGraw-Hall, New York (1988).
36N. Belavendram, Quality by Design, Prentice-Hall, London (1995).
37Y. S. Tarng and W. H. Yang, “Optimization of the Weld Bead Geometry in Gas Tungsten Arc Welding by the Taguchi Method,” The International Journal of Advanced Manufacturing Technology, Vol. 14, pp. 549-554 (1998).
38湯燦泰，熔融紡絲系統參數最佳化之研究，台灣科技大學高分子系碩士論文 (2001)。
39胡昌柴、李國柴、劉濤、周志杰，基於Matlab 6.x的系統分析與設計-小波分析，西安電子科技大學出版社 (2004)。
40MATLAB Wavelet Toolbox User’s Guide Version 2.
41張斐章、張麗秋，類神經網路，東華書局 (2005)。
42S. Haykin, Neural Network：A Comprehensive Foundation, Prentice-Hall (1999).
43溫耀銘，應用小波封包轉換與類神經網路於針織物瑕疵辨識，台灣科技大學高分子系碩士論文 (2002)。
44葉怡成，類神經網路模式應用與實作，儒林圖書有限公司 (2000)。
45A. K. Tiwari, K. K. Shukla, “A Comparative Study of Fuzzy Inferencing Systems Using Discrete Wavelet Transforms in Feature Extraction for the Detection and Classification of Power Quality,” Processdings of Fuzzy Set Theory and its Mathematical Aspects and Applicatons, pp. 220-227 (2002).
46孫延奎，小波分析及其應用，機械工業出版社 (2005)。