VM-based Baseline-Predictive-Maintenance (BPM) Scheme已在近期被提出，其可達成機台設備零件之錯誤診斷及剩餘壽命之預測；而且其無需龐大歷史失效紀錄資訊的特性，使得BPM在預防保養後將有機會達成自動化模型建構之目的，進而讓BPM更適用於產線上。本論文之目的為開發Automatic Baseline-Sample-Selection (ABSS) Scheme，自動地選取BPM Scheme所需之建模樣本。在歷史資料可收集到的情形下，ABSS Scheme採用了healthy-samples-selection (HSS) 和dynamic-moving-window (DMW) 等方法來自動地挑選精簡且健康之歷史樣本 (concise-and-healthy samples, C&H samples)。而在歷史資料無法收集到的情形下，ABSS Scheme採用了standard-deviation-determination (SDD) 方法來解決新鮮穩定之少量建模樣本所導致的模型過於敏感的問題。ABSS Scheme也應用了殘差分析與假設檢定等方法來濾除模型內之矛盾樣本 (contradictory samples)，因為此類樣本將對BPM的結果產生負面影響。最後經由實驗驗證可得知，ABSS Scheme可決定合適的BPM建模樣本，進而達成自動化模型建構之目的。

The baseline-predictive-maintenance (BPM) scheme based on virtual-metrology technology was proposed recently. By applying the BPM scheme, fault diagnosis and prognosis can be accomplished and the requirement of massive historical failure data can also be released. Due to the merit of not requiring historical failure data, automatic creation of a BPM model just after maintenance becomes possible. This makes the BPM scheme more applicable for on-line implementation. The purpose of this paper is to develop an automatic baseline-sample-selection (ABSS) scheme to automatically prepare the modeling samples for creating the BPM model. The so-called healthy-samples-selection (HSS) and dynamic-moving-window (DMW) methods are adopted in the ABSS scheme to automatically select the historical concise-and-healthy (C&H) samples when historical data are available. If historical data are unavailable, an ad hoc z-score standard-deviation-determination (SDD) method of fresh modeling samples is applied in the ABSS scheme to remedy the problem of overestimating the rarity of the small amount of fresh modeling samples. Residual analysis and hypothesis testing are also applied in the ABSS scheme for deleting contradictory samples, which may deteriorate the BPM results. Experimental results show that the ABSS scheme can prepare proper modeling samples such that automatic model creation for BPM can be accomplished.

[1] J. Hollister and P. McGuire, Research on the Current Status of Predictive Maintenance (PdM) Algorithms and Applications, ISMI Predictive and Preventive Maintenance Initiative, Alan Weber and Associates, Inc., February, 2009.
[2] ISMI Predictive and Preventive Maintenance Equipment Implementation Guidelines, Technology Transfer #08064934A-ENG International SEMATECH Manufacturing Initiative, August 30, 2008.
[3] ISMI Consensus Preventive and Predictive Maintenance Vision Guideline: Version 1.1 Technology Transfer #06114819C-ENG International SEMATECH Manufacturing Initiative, November 20, 2007.
[4] J. Liu, D. Djurdjanovic, J. Ni, N. Casoetto, and J. Lee, "Similarity Based Method for Manufacturing Process Performance Prediction and Diagnosis," Computers in Industry, vol. 58, pp.558-566, January 2007.
[5] H.-E. Kim, A. C.C. Tan, J. Mathew, and B.-K. Choi, "Bearing Fault Prognosis Based on Health State Probability Estimation," Expert Systems with Applications, vol. 39, pp. 5200-5213, April 2012.
[6] Y.-S. Hsieh, F.-T. Cheng, H.-C. Huang, C.-R. Wang, S.-C. Wang, and H.-C. Yang, “VM-based Baseline Predictive Maintenance Scheme,” IEEE Transactions on Semiconductor Manufacturing, vol. 26, no. 1, pp. 132-144, February 2013.
[7] H. Müller, U. Leser, J.-C. Freytag, "Mining for Patterns in Contradictory Data," Proc. of the 1st Intl. ACM Workshop on Information Quality in Information Systems (IQIS 2004), p. 51-58, France, June 2004.
[8] C.-F. Chen, Y.-S. Hsieh, F.-T. Cheng, H.-C. Huang, and S.-C. Wang, “Automatic Baseline-Sample-Selection Scheme for Baseline Predictive Maintenance,” Proc. of the 2013 IEEE International Conference on Automation Science and Engineering (CASE 2013), Madison, Wisconsin, USA, pp. 189-194, August 17-21, 2013.
[9] A. K. Jain, K. Nandakumar, and A. Ross, “Score Normalization in Multimodal Biometric Systems,” Pattern Recognit., vol. 38, no. 12, pp.2270–2285, Dec. 2005.
[10] J.R. Crawford, D.C. Howell. "Comparing an Individual's Test Score against Norms Derived from Small Samples." The Clinical Neuropsychologist,12 (1998), p.p.482-486.
[11] R. Stine and D. Foster, Statistics for Business: Decision Making and Analysis, Addison-Wesley, 2011.
[12] J.R. Crawford, P.H. Garthwaite "Investigation of the Single Case in Neuropsychology: Confidence Limits on the Abnormality of Test Scores and Test Score Differences." Neuropsychologia 40.8 (2002), p.p. 1196-1208.
[13] Y.-T. Huang and F.-T. Cheng, “Automatic Data Quality Evaluation for the AVM System,” IEEE Transactions on Semiconductor Manufacturing, vol. 24, no. 3, pp. 445-454, Aug. 2011.
[14] W.-M. Wu, F.-T. Cheng and F.-W. Kong, “A Dynamic-Moving-Window Scheme for Virtual-Metrology Model Refreshing,” IEEE Transactions on Semiconductor Manufacturing, vol. 25, pp. 238-246, May 2012.
[15] R. L. Mason, R. F. Gunst, and J. L. Hess, Statistical Design and Analysis of Experiments with Applications to Engineering and Science, New York: Wiley, 1989.
[16] F.-T. Cheng, Y.-T. Chen, Y.-C. Su, and D.-L. Zeng, "Evaluating Reliance Level of a Virtual Metrology System," IEEE Transactions on Semiconductor Manufacturing, vol. 21, no. 1, pp. 92-103, February 2008.
[17] S. Washietl, I. Hofacker and P. Stadler. "Fast and Reliable Prediction of Noncoding RNAs." Proc. Natl Acad. Sci. vol. 102, USA, pages 2454–2459, 2005.
[18] Bollen, A. Pepe, and H. Mao. "Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena." Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media. 2011.
[19] K. Yang and B. El-Haik, Design for Six Sigma: A Roadmap for Product Development, McGraw-Hill, 2003.
[20] D. C. Montgomery, G. C. Runger, N. F. Hubele, Engineering Statistics, 2nd Edition, Wiley,  New York, 2001.
[21] Zong Qun, Dou Liqian, Sun Liankun, Liu Wenjing “Model Validation Based on Residuals Analysis Method” Proceedings of the 27th Chinese Control Conference, Kunming,Yunnan, China, Jul. 2008.
[22] R. M. Heiberger and B. Holland, Statistical Analysis and Data Display: An Intermediate Course with Examples in S-Plus, R, and SAS, Springer, New York, 2004.
[23] M. L. Marx and R. J. Larsen, Introduction to Mathematical Statistics and Its Applications. Pearson/Prentice Hall, 2006.