Fault-Detection Method for Batch Process Based on Sparse Distance
ZHANG Cheng1,2, LI Yuan2, GAO Xianwen1
1. School of Information Science and Technology, Northeastern University, Shenyang 110819, China;
2. Research Center for Technical Process Fault Diagnosis and Safety, Shenyang University of Chemical Technology, Shenyang 110142, China
Aiming at the features of multiple product processes, nonlinear and non-Gaussian, a fault detection method in batch process based on sparse distance (FD-SD) is proposed. Sparse distance is used to measure the density of training samples around a test sample and to analyze the training samples distribution feature near the test sample. Sparse distance is calculated by cumulative distribution function of sample distance through kernel density estimate function with changed window width. The control limit is calculated by cumulative distribution function of sparse distance, and then the detection model based on sparse distance is built. FD-SD does not needs the hypothesis of variables obeying Gauss and linear distribution and can improve the accuracy and reliability of the fault detection process. From Simulation results in artificial case and semiconductor etch batch process, it is shown that FD-SD can detect fault in nonlinear and multi-mode processes. The validity of FD-SD is proved by the results.
张成, 李元, 高宪文. 基于稀疏距离的间歇过程故障检测方法[J]. 信息与控制, 2014, 43(5): 588-595.
ZHANG Cheng, LI Yuan, GAO Xianwen. Fault-Detection Method for Batch Process Based on Sparse Distance. Information and control, 2014, 43(5): 588-595.
Singh K P, Malik A. Multi-way partial least squares modeling of water quality data[J]. Analytica Chimica Acta, 2007, 584(2): 385-396.
[19]
张成, 李元. 基于统计模量分析间歇过程故障检测方法研究[J]. 仪器仪表学报, 2013, 34(9): 2103-2110. Zhang C, Li Y. Study on the fault-detection method in batch process based on statistical pattern analysis[J]. Chinese Journal of Scientific Instrument, 2013, 34(9): 2103-2110.
[20]
盛骤, 谢式千, 潘承毅. 概率论与数理统计[M]. 5版. 杭州: 浙江大学出版社, 2001: 213-218. Sheng Z, Xie S Q, Pan C Y. Probability theory and mathematical statistics[M]. 5th ed. Hangzhou: Zhejiang University Press, 2001: 213-218.
[22]
He Q P, Wang J. Fault detection using the k-nearest neighbor rule for semiconductor manufacturing processes[J]. IEEE Transactions on Semiconductor Manufacturing, 2007, 20(4): 345-354.
[1]
Hung H, Wu P, Tu I, et al. On multilinear principal component analysis of order-two tensors[J]. Biometrika, 2012, 99(3): 569-583.
[6]
Tracy N, Young J, Mason R. Multivariate control charts for individual observations[J]. Journal of Quality Technology, 1992, 24(2): 88-95.
[11]
Dayal B S, MacGregor J F. Recursive exponentially weighted PLS and its applications to adaptive control and prediction[J]. Journal of Process Control, 1997, 7(3): 169-179.
[16]
Scholkopf B, Mika S, Burges C J C. Input space versus feature space in kernel based methods[J]. IEEE Transactions on Neural Networks, 1999, 10(5): 1000-1017.
[18]
Kano M, Sakata T, Hasebe S. Just-in-time statistical process control: Adaptive monitoring of vinyl acetate monomer process[C]//The 18th IFAC World Congress. 2011: 13157-13162.
[5]
Wong J. Batch PLS analysis and FDC process control of within lot SiON gate oxide thickness variation in sub-nanometer range[C]//Proceedings of AEC/APC Symposium XVIII. 2006.
[10]
Choi S W, Martin E B, Morris A J. Adaptive multivariate statistical process control for monitoring time-varying processes[J]. Industrial & Engineering Chemistry Research, 2006, 45(9): 3108-3118.
[15]
Wang X, Kruger U, Lennox B. Recursive partial least squares algorithms for monitoring complex industrial processes[J]. Control Engineering Practice, 2003, 11(6): 613-632.
[21]
Eigenvector Research Inc. Metal etch data for fault detection evaluation[DB/OL]. (2013-01-12)[2013-07-20]. http://software.eigenvector.com/Data/Etch/index.html.
[2]
周东华, 李钢, 李元. 数据驱动的过程故障检测与诊断技术[M]. 北京: 科学出版社, 2011.Zhou D H, Li G, Li Y. Data driven based process fault detection and diagnosis technology[M]. Beijing: Science Press, 2011.
[7]
Jackson J E, Mudholkar G S. Control procedures for residuals associated with principal component analysis[J]. Technometrics, 1979, 21(3): 341-349.
[8]
Gunther J C, Conner J S, Seborg D E. Process monitoring and quality variable prediction utilizing PLS in industrial fed-batch cell culture[J]. Journal of Process Control, 2009, 19(5): 914-921.
[12]
Lee D S, Vanrolleghem P A. Adaptive consensus principal component analysis for on-line batch process monitoring[J]. Environmental Monitoring and Assessment, 2004, 92(1/2/3): 119-135.
[13]
Li W H, Yue H H. Recursive PCA for adaptive process monitoring[J]. Journal of Process Control, 2000, 10(5): 471-486.
[17]
Cui P L, Li J H, Wang G Z. Improved kernel principal component analysis for fault detection[J]. Expert Systems with Applications, 2008, 34(2): 1210-1219.
[4]
Yu J. Fault detection using principal components-based Gaussian mixture model for semiconductor manufacturing processes[J]. IEEE Transactions on Semiconductor Manufacturing, 2011, 24(3): 432-444.
[9]
Zhao S J, Zhang J, Xu Y M. Monitoring of processes with multiple operating modes through multiple principle component analysis models[J]. Industrial & Engineering Chemistry Research, 2004, 43(22): 7025-7035.
[14]
Agarwal A, El-Ghazawi T, El-Askary H, et al. Efficient hierarchical-PCA dimension reduction for hyperspectral imagery[C]//2007 IEEE International Symposium on Signal Processing and Information Technology. Piscataway, NJ, USA: IEEE, 2007: 353-356.
[3]
Wang J, He Q P, Qin S J, et al. Recursive least squares estimation for run-to-run control with metrology delay and its application to STI etch process[J]. IEEE Transactions on Semiconductor Manufacturing, 2005, 18(2): 309-319.
