企業為了使製程穩定以增進產品之品質，通常使用管制圖來監控製程，當製程失去控制時管制圖會發出警訊，品管人員可以即時介入以避免造成損失，因此發展了許多管制圖因應不同之需求。但是有些產業之製程特性和抽樣檢測方法限制其能使用之管制圖種類，當製程每次只能抽樣一個樣本時，大多數的管制圖都無法適用，個別管制圖便是在這種限制之下發展而成的管制圖。個別管制圖針對每次只有一個樣本的情況，其放鬆管制界線以減少誤判之機率，但同時導致當偏移發生時，其發出警訊之時間會延遲，導致品管人員無法即時的分析製程偏移之原因。而針對個別管制圖之研究很少，最根本之原因便是其樣本數過少，導致統計假設無法符合，因此無法得知其製程偏移之改變點，因此利用類神經網路自我學習之特性，得知製程之偏移資訊，為一可行之改善方法。鑑此本研究建立兩個類神經網路，第一個模型為偵測模型，利用卷積神經網路偵測製程偏移資訊，在製程的平均數偏移時能夠偵測其偏移之時間點和偏移幅度，當可以獲知更精確之偏移資訊，品管人員便可以快速的找出偏移之原因；第二個模型為監控模型，建立長短期記憶網路以取代個別管制圖監控製程之穩定性，以期快速且準確的監控製程之平均數是否偏移。本研究建立的兩個類神經網路模型可以有效的彌補個別管制圖的缺點，模型一中偵測製程偏移時間誤差在兩個抽樣點的機率可以達到90%，而判斷其偏移幅度之準確度達到95%，而在模型二中，其平均延遲時間可以縮小到兩個觀測點內，兩個模型均有效的解決個別管制圖之缺點。

A control chart is a common tool that enterprises use to monitor their process stability. Because a control chart can effectively decrease costs, many different types of control charts have been developed for different purposes. However, not all industries can use common control charts. Due to the process itself or sampling method, these industries only sample one product at a time, so an individual control chart is designed for these situations. Because of a small sampling size, individual control charts have two problems. First, an individual control chart will delay signaling of an alarm because its control limits are looser than other alternatives that decrease the probability of a false alarm rate. Second, it can not be used to calculate statisics, such as mean and variance, so it is difficult to learn more about the shifted data process. To resolve these problems, artificial neural networks (ANNs) are used in this study to build two models. The first model is a detection model that uses a convolution neural network (CNN) to find the change point information, including what time and how much of the margin the process changed. This model developed can achieve a 90% accuracy rate in terms of changing time and a 95% accuracy rate for the margin if two observation errors are allowed. The second model is a monitor model that uses a long short-term memory network (LSTM) that can be substituted for the role of the individual control chart. The proposed method is more effective than traditional methods.

Abdoljalil, A. Aminollah, K. Noorbakhsh, A. G. (2018). Control chart pattern recognition using RBF neural network with new training algorithm and practical features. ISA Transactions, 79, 202–216.
Atashgar, K. (2013). Identification of the change point: an overview. The International Journal of Advanced Manufacturing Technology, 64(9-12), 1663–1683.
Ahmadzadeh, F. (2018). Change point detection with multivariate control charts by artificial neural network. The International Journal of Advanced Manufacturing Technology, 97, 3179-3190.
Chang, S. I. & Aw, C. A. (1996). A neural fuzzy control chart for detecting and classifying process mean shifts. International Journal of Production Research, 34(8), 2265-2278.
Cheng, C. S. (1994). Detecting changes in the process mean using artificial neural networks approach. Journal of Chinese Institute of Industrial Engineers, 11, 47-54.
Cortes, C., & Vapnik, V. (1995). Support-vector network. Machine Learning, 20(3), 273-297.
Du, S., Lv, J. & Xi, L. (2012). On-line classifying process mean shifts in multivariate control charts based on multiclass support vector machines. International Journal of Production Research, 50(22), 6288-6310.
Habibi, R. (2011). Change point detection using bootstrap methods, Advanced Modeling and Optimizing, 13(3), 341-347
Hartigan, J.A. (1990). Partition model. Communication in Statistics Theory and Methods, 19, 2745-2756.
Héctor De la Torre Gutiérrez & Duc Truong Pham. (2018). Identification of patterns in control charts for processes with statistically correlated noise. International Journal of Production Research, 56(4), 1504-1520.
Hinkley D.V. (1970). Inferences about the change-point in a sequence of random variables. Biometrika, 57(1), 1-17.
Hinton, G.E., Osindero, S., & Teh, Y.W. (2006). A fast learning algorithm for deep belief nets. Neural Comp, 18, 1527-1554.
Kaung, D.L., Zhang, J., Ferguson, K., & Steele, C. (2013). International Conference on Natural Computation. Reliable modeling of chemical duarability of high level waste glass using bootstrap aggregated neural networks, 178-183.
Kooi, H. N., Midi H, Kok H. N. (2017). Change Point Detection of Robust Individuals Control Chart. International Journal of Industrial Engineering, 24(5), 526-541.
Noorossana, R., & Shadman, A. (2009). Estimating the change point of a normal process mean with a monotonic change. Quality and Reliability Engineering International, 25(1), 79-90.
Page, E.S. (1954). Continuous inspection schemes. Biometrika, 41(1-2), 100–115.
Perry, M.B., & Pignatiello, J.J. (2006). Estimation of the change point of a normal process mean with linear trend disturbance in SPC. Quality Technology & Quantitative Management, 3(3), 325-334.
Pignatiello, J. J., & Samuel, T. R. (2001). Estimation of the change point of a normal process mean in SPC application. Quality Technology & Quantitative Management, 33(1), 82-95.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533-536.
Salehi, M., Kazemzadeh, R. B. & Salmasnia, A. (2012). On line detection of mean and variance shift using neural networks and support vector machine in multivariate processes. Applied Soft Computing, 12(9), 2973-2984.
Samuel, T. R., Pignatiello, J. J., & Calvin, J. A. (1998). Identifying the time of a step change with X bar control chart. Quality Engineering, 10(3), 521-527.
Shao, Y. E., & Chiu, C.C. (2016). Applying emerging soft computing approaches to control chart pattern recognition for an SPC-EPC process. Neurocomputing, 201, 19-28.
Smith, A.E. (1994). X-bar and R control chart interpretation using neural computing. International Journal of Production Research, 32(2), 309-320.
Steven E.R., Emma N.C., & Charles W.C. (1994). Design Strategies for Individuals and Moving Range Control Charts. Journal of Quality Technology, 26(4), 274-287.
Sullivan, J.H. (2002). Detection of multiple change point from clustering individual observations. Journal of Quality Technology, 34(4), 371-383.
Velasco, T., & Rowe, M.R. (1993). Back Propagation Artificial Neural Networks for The Analysis of Quality Control Charts. Computers & Industrial Engineering, 25(4), 397-400.
Wang, T. Y. & Chen, L. H. (2002). Mean shifts detection and classification in multivariate process: a neural-fuzzy approach. Journal of Intelligent Manufacturing, 13(3), 211-221.
Wu, C., Liu, F. & Zhu, B. (2015). Control chart pattern recognition using an integrated model based on binary-tree support vector machine. International Journal of Production Research, 53(7), 2026-2040.
Yi, J., Prybutok, V. R., & Clayton, H. R. (2001). ARL comparisons between neural network models and x-bar control charts for quality characteristics that are nonnormally distributed. Economic Quality Control, 16(1), 5-15.
Yu, J. B. & Xi, L. F. (2009). A neural network ensemble-based model for on-line monitoring and diagnosis of out of control signals in multivariate manufacturing processes. Expert Systems with Applications, 36, 909–921.
Zio, E. (2006). A study of the bootstrap method for estimating the accuracy of artificial neural networks in predicting nuclear transient processes. IEEE Transactions on Nuclear Science, 53(3), 1460-1478