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研究生:褚慧芸
研究生(外文):Huei-Yun Chu
論文名稱:基於時間序列預測的機器良率預測
論文名稱(外文):Machine Yield Rate Forecasting Based on Time-Series Forecasting
指導教授:梁德容梁德容引用關係
指導教授(外文):Deron Liang
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:60
中文關鍵詞:陶瓷基板機器良率暗裂破片時間序列預測XGBoostOnline Learning
外文關鍵詞:Ceramic substrateMachine yield rateMicro-crack piecesTime series forecastingXGBoostOnline Learning
相關次數:
  • 被引用被引用:1
  • 點閱點閱:160
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
摘要 i
Abstract ii
目錄 iv
圖目錄 vi
表目錄 vii
一、 緒論 1
1-1研究背景 1
1-2研究動機 3
1-3研究貢獻 3
1-4論文架構 4
二、 相關研究 5
2-1以EM演算法估計過去的機器良率之方法 5
2-2時間序列預測模型 7
三、 問題定義與研究 14
3-1問題定義 14
3-2目標式定義 15
四、 實驗與討論 18
4-1實驗資料集 18
4-2 資料前處理 20
4-3實驗一:EM (Previous)、ARIMA、XGBoost比較 23
4-4實驗二:XGBoost特徵數調整 32
4-5實驗三:以週為單位及天為單位的預測機器良率比較 37
五、 應用 40
5-1機器維修預警 40
5-2機器排程推薦 42
六、 結論與未來展望 44
6-1結論 44
6-2未來展望 44
參考文獻 46
[1] Y. Xiang, C. R. Cassady, T. Jin and C. W. Zhang, "Joint production and maintenance planning with machine deterioration and random yield," International Journal of Production Research, 2014.
[2] R. Raj Mohan, K. Thiruppathi, R. Venkatraman and S. Raghuraman, "Quality Improvement through First Pass Yield using Statistical Process Control Approach," Journal of Applied Sciences, vol. 12, pp. 985-991, 2012.
[3] G. Vogel, "Avoiding flex cracks in ceramic capacitors: Analytical tool for a reliable failure analysis and guideline for positioning cercaps on PCBs," Microelectronics Reliability, vol. 55, 6 2015.
[4] N. V. Hop and N. Nagarur, "The scheduling problem of PCBs for multiple non-identical parallel machines," European Journal of Operational Research, vol. 158, pp. 577-594, 11 2014.
[5] P.-K. Huang and D. Liang, "Analyze the micro-crack rate of PCB based on Expectation-Maximization algorithm," 2019.
[6] A. P. Dempster, N. M. Laird and D. B. Rubin, "Maximum Likelihood from Incomplete Data Via the EM Algorithm," Journal of the Royal Statistical Society, vol. 39, pp. 1-22, 1977.
[7] M. S. Gold and P. M. Bentler, "Treatments of Missing Data: A Monte Carlo Comparison of RBHDI, Iterative Stochastic Regression Imputation, and Expectation-Maximization," Structural Equation Modeling: A Multidisciplinary Journal, pp. 319-355, 11 2009.
[8] G. E. P. Box, G. M. Jenkins, G. C. Reinsel and G. M. Ljung, Time Series Analysis: Forecasting and Control 5th ed., Wiley, 2015.
[9] A. A. Ariyo, A. O. Adewumi and C. K. Ayo, "Stock Price Prediction Using the ARIMA Model," 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, pp. 106-112, 2014.
[10] P.-F. Pai and C.-S. Lin, "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, vol. 33, pp. 497-505, 12 2005.
[11] P. Mondal, L. Shit and S. Goswami, "Study of Effectiveness of Time Series Modeling (Arima) in Forecasting Stock Prices," International Journal of Computer Science, Engineering and Applications, pp. 13-29, 4 2014.
[12] J. Contreras, R. Espinola, F. J. Nogales and A. J. Conejo, "ARIMA models to predict next-day electricity prices," IEEE Transactions on Power Systems, vol. 18, no. 3, pp. 1014-1020, 8 2003.
[13] A. A. Adebiyi, A. O. Adewumi and C. K. Ayo, "Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction," Journal of Applied Mathematics, vol. 2014, pp. 1-7, 3 2014.
[14] E. Cadenas and W. Rivera, "Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA-ANN model," Renewable Energy, vol. 35, pp. 2732-2738, 12 2010.
[15] T. Ozaki, "On the Order Determination of Arima Models," Journal of the Royal Statistical Society: Series C (Applied Statistics), vol. 26, no. 3, pp. 290-301, 1977.
[16] T. Chen and C. Guestrin, "XGBoost: A Scalable Tree Boosting System," in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.
[17] M. Gumus and M. S. Kıran, "Crude oil price forecasting using XGBoost," in 2017 International Conference on Computer Science and Engineering (UBMK), 2017.
[18] R. A. Abbasi, N. Javaid, M. N. J. Ghuman, Z. A. Khan, S. U. Rehman and Amanullah, "Short Term Load Forecasting Using XGBoost," in Web, Artificial Intelligence and Network Applications, Cham, Springer International Publishing, 2019, pp. 1120-1131.
[19] M. Mohri, A. Rostamizadeh and A. Talwalkar, "On-Line Learning," in Foundations of Machine Learning, MIT Press, 2012, pp. 147-182.
[20] H. Akaike, "A new look at the statistical model identification," IEEE Transactions on Automatic Control, vol. 19, pp. 716-723, 1974.
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