Bayesian cross-product quality control via transfer learning
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Kai Wang | Kai Wang | Fugee Tsung | F. Tsung | Kai Wang
[1] Ronald J. M. M. Does,et al. Guaranteed In-Control Performance for the Shewhart X and X Control Charts , 2017 .
[2] William H. Woodall,et al. The Difficulty in Designing Shewhart X̄ and X Control Charts with Estimated Parameters , 2015 .
[3] Satish T. S. Bukkapatnam,et al. The internet of things for smart manufacturing: A review , 2019, IISE Trans..
[4] Ronald J. M. M. Does,et al. The effect of continuously updating control chart limits on control chart performance , 2019, Qual. Reliab. Eng. Int..
[5] Qingyu Yang,et al. A new Bayesian scheme for self-starting process mean monitoring , 2020 .
[6] Andrew Y. C. Nee,et al. Digital twin-driven product design framework , 2019, Int. J. Prod. Res..
[7] Sajid Ali,et al. A predictive Bayesian approach to sequential time‐between‐events monitoring , 2019, Qual. Reliab. Eng. Int..
[8] Marko Robnik-Sikonja. Data Generators for Learning Systems Based on RBF Networks , 2016, IEEE Transactions on Neural Networks and Learning Systems.
[9] Charles P. Quesenberry,et al. DPV Q charts for start-up processes and short or long runs , 1991 .
[10] Andrew Kusiak,et al. From data to big data in production research: the past and future trends , 2019, Int. J. Prod. Res..
[11] Ronald J. M. M. Does,et al. Correction factors for Shewhart and control charts to achieve desired unconditional ARL , 2016 .
[12] Furong Gao,et al. Process similarity and developing new process models through migration , 2009 .
[13] Der-Chiang Li,et al. A genetic algorithm-based virtual sample generation technique to improve small data set learning , 2014, Neurocomputing.
[14] Alireza Faraz,et al. An exact method for designing Shewhart and S2 control charts to guarantee in-control performance , 2018, Int. J. Prod. Res..
[15] Daniel W. Apley,et al. Posterior Distribution Charts: A Bayesian Approach for Graphically Exploring a Process Mean , 2012, Technometrics.
[16] Jean-Yves Tourneret,et al. Change Detection in Multisensor SAR Images Using Bivariate Gamma Distributions , 2008, IEEE Transactions on Image Processing.
[17] Angappa Gunasekaran,et al. Big data in lean six sigma: a review and further research directions , 2019, Int. J. Prod. Res..
[18] Der-Chiang Li,et al. Using mega-trend-diffusion and artificial samples in small data set learning for early flexible manufacturing system scheduling knowledge , 2007, Comput. Oper. Res..
[19] Axel Gandy,et al. Guaranteed Conditional Performance of Control Charts via Bootstrap Methods , 2011, 1111.4180.
[20] Ulrich Menzefricke,et al. ON THE EVALUATION OF CONTROL CHART LIMITS BASED ON PREDICTIVE DISTRIBUTIONS , 2002 .
[21] Huaguang Zhang,et al. A Small-Sample Wind Turbine Fault Detection Method With Synthetic Fault Data Using Generative Adversarial Nets , 2019, IEEE Transactions on Industrial Informatics.
[22] Steven X. Ding,et al. A Review on Basic Data-Driven Approaches for Industrial Process Monitoring , 2014, IEEE Transactions on Industrial Electronics.
[23] Sofia Panagiotidou,et al. A sequential monitoring Bayesian control scheme for attributes , 2018, Quality Technology & Quantitative Management.
[24] Tomaso Poggio,et al. Incorporating prior information in machine learning by creating virtual examples , 1998, Proc. IEEE.
[25] Wilbert C.M. Kallenberg,et al. Estimation in Shewhart control charts: effects and corrections , 2004 .
[26] Charles W. Champ,et al. Effects of Parameter Estimation on Control Chart Properties: A Literature Review , 2006 .
[27] William H. Woodall,et al. Another Look at the EWMA Control Chart with Estimated Parameters , 2015 .
[28] Douglas C. Montgomery,et al. Some Current Directions in the Theory and Application of Statistical Process Monitoring , 2014 .
[29] Philippe Castagliola,et al. Some Recent Developments on the Effects of Parameter Estimation on Control Charts , 2014, Qual. Reliab. Eng. Int..
[30] Jean-Yves Tourneret,et al. Bivariate Gamma Distributions for Image Registration and Change Detection , 2007, IEEE Transactions on Image Processing.
[31] Ke Zhang,et al. Statistical transfer learning: A review and some extensions to statistical process control , 2018 .
[32] Anis Chelbi,et al. Integrated production, statistical process control, and maintenance policy for unreliable manufacturing systems , 2018, Int. J. Prod. Res..
[33] Zhibin Jiang,et al. A conjugate Bayesian approach to control chart for multi-batch and low volume production , 2015 .
[34] Zhonghua Li,et al. Self-starting control chart for simultaneously monitoring process mean and variance , 2010 .
[35] Philippe Bernardoff,et al. Which multivariate gamma distributions are infinitely divisible , 2006 .
[36] Nan-Jung Hsu,et al. Joint modeling of laboratory and field data with application to warranty prediction for highly reliable products , 2016 .
[37] Jieping Ye,et al. A transfer learning approach for network modeling , 2012, IIE transactions : industrial engineering research & development.
[38] Douglas M. Hawkins,et al. A Bayesian Scheme to Detect Changes in the Mean of a Short-Run Process , 2005, Technometrics.
[39] Claudio Moraga,et al. A diffusion-neural-network for learning from small samples , 2004, Int. J. Approx. Reason..
[40] Der-Chiang Li,et al. Using past manufacturing experience to assist building the yield forecast model for new manufacturing processes , 2012, J. Intell. Manuf..
[41] Douglas C. Montgomery,et al. A review of statistical process control techniques for short run manufacturing systems , 1996 .
[42] Stefan H. Steiner,et al. An Overview of Phase I Analysis for Process Improvement and Monitoring , 2014 .