Generalized grouped contributions for hierarchical fault diagnosis with group Lasso

Control Engineering Practice - Tập 93 - Trang 104193 - 2019
Chao Shang1, Hongquan Ji2, Xiaolin Huang3, Fan Yang1, Dexian Huang1
1Department of Automation, Tsinghua University, and Beijing National Research Center for Information Science and Technology, Beijing 100084, China
2College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
3Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200400, China

Tài liệu tham khảo

Alcala, 2009, Reconstruction-based contribution for process monitoring, Automatica, 45, 1593, 10.1016/j.automatica.2009.02.027 Alcala, 2011, Analysis and generalization of fault diagnosis methods for process monitoring, Journal of Process Control, 21, 322, 10.1016/j.jprocont.2010.10.005 Chiang, 2000, Fault diagnosis in chemical processes using fisher discriminant analysis, discriminant partial least squares, and principal component analysis, Chemometrics and Intelligent Laboratory Systems, 50, 243, 10.1016/S0169-7439(99)00061-1 Downs, 1993, A plant-wide industrial process control problem, Computers & Chemical Engineering, 17, 245, 10.1016/0098-1354(93)80018-I Dunia, 1998, Subspace approach to multidimensional fault identification and reconstruction, AIChE Journal, 44, 1813, 10.1002/aic.690440812 Efron, 2004, Least angle regression, The Annals of Statistics, 32, 407, 10.1214/009053604000000067 Eldar, 2012 Fan, 2018, Identification of robust probabilistic slow feature regression model for process data contaminated with outliers, Chemometrics and Intelligent Laboratory Systems, 173, 1, 10.1016/j.chemolab.2017.12.009 Feng, 2015, Fault location using wide-area measurements and sparse estimation, IEEE Transactions on Power Systems, 31, 2938, 10.1109/TPWRS.2015.2469606 Gao, 2015, Detecting and isolating plant-wide oscillations via slow feature analysis, 906 Ge, 2017, Data mining and analytics in the process industry: The role of machine learning, IEEE Access, 5, 20590, 10.1109/ACCESS.2017.2756872 He, 2012, Reconstruction-based multivariate contribution analysis for fault isolation: A branch and bound approach, Journal of Process Control, 22, 1228, 10.1016/j.jprocont.2012.05.010 Huang, 2018, Nonconvex penalties with analytical solutions for one-bit compressive sensing, Signal Processing, 144, 341, 10.1016/j.sigpro.2017.10.023 Hyvärinen, 2000, Independent component analysis: Algorithms and applications, Neural Networks, 13, 411, 10.1016/S0893-6080(00)00026-5 Ji, 2018, Exponential smoothing reconstruction approach for incipient fault isolation, Industrial and Engineering Chemistry Research, 57, 6353, 10.1021/acs.iecr.8b00478 Ji, 2016, On the use of reconstruction-based contribution for fault diagnosis, Journal of Process Control, 40, 24, 10.1016/j.jprocont.2016.01.011 Jiang, 2015, Multiblock independent component analysis integrated with Hellinger distance and Bayesian inference for non-Gaussian plant-wide process monitoring, Industrial and Engineering Chemistry Research, 54, 2497, 10.1021/ie403540b Jiang, 2015, Nonlinear plant-wide process monitoring using MI-spectral clustering and Bayesian inference-based multiblock KPCA, Journal of Process Control, 32, 38, 10.1016/j.jprocont.2015.04.014 Jiang, 2019, Review and perspectives of data-driven distributed monitoring for industrial plant-wide processes, Industrial and Engineering Chemistry Research, 58, 12899, 10.1021/acs.iecr.9b02391 Kariwala, 2010, A branch and bound method for isolation of faulty variables through missing variable analysis, Journal of Process Control, 20, 1198, 10.1016/j.jprocont.2010.07.007 Kim, 2018, Efficient process monitoring via the integrated use of Markov random fields learning and the graphical Lasso, Industrial and Engineering Chemistry Research, 57, 13144, 10.1021/acs.iecr.8b02106 Ku, 1995, Disturbance detection and isolation by dynamic principal component analysis, Chemometrics and Intelligent Laboratory Systems, 30, 179, 10.1016/0169-7439(95)00076-3 Lee, 2004, Statistical monitoring of dynamic processes based on dynamic independent component analysis, Chemical Engineering Science, 59, 2995, 10.1016/j.ces.2004.04.031 Lee, 2004, Statistical process monitoring with independent component analysis, Journal of Process Control, 14, 467, 10.1016/j.jprocont.2003.09.004 Liu, 2012, Fault diagnosis using contribution plots without smearing effect on non-faulty variables, Journal of Process Control, 22, 1609, 10.1016/j.jprocont.2012.06.016 Liu, 2012, Fault diagnosis of continuous annealing processes using a reconstruction-based method, Control Engineering Practice, 20, 511, 10.1016/j.conengprac.2012.01.005 Liu, 2014, Bayesian filtering of the smearing effect: Fault isolation in chemical process monitoring, Journal of Process Control, 24, 1, 10.1016/j.jprocont.2013.12.018 Liu, 2019, Structured joint sparse principal component analysis for fault detection and isolation, IEEE Transactions on Industrial Informatics, 15, 2721, 10.1109/TII.2018.2868364 Lyman, 1995, Plant-wide control of the Tennessee Eastman problem, Computers & Chemical Engineering, 19, 321, 10.1016/0098-1354(94)00057-U MacGregor, 2012, Monitoring, fault diagnosis, fault-tolerant control and optimization: Data driven methods, Computers & Chemical Engineering, 47, 111, 10.1016/j.compchemeng.2012.06.017 MacGregor, 1994, Process monitoring and diagnosis by multiblock pls methods, AIChE Journal, 40, 826, 10.1002/aic.690400509 Miller, 1998, Contribution plots: A missing link in multivariate quality control, Applied Mathematics and Computer Science, 8, 775 Ohlsson, 2014, Scalable anomaly detection in large homogeneous populations, Automatica, 50, 1459, 10.1016/j.automatica.2014.03.008 Qin, 2003, Statistical process monitoring: Basics and beyond, Journal of Chemometrics, 17, 480, 10.1002/cem.800 Qin, 2012, Survey on data-driven industrial process monitoring and diagnosis, Annual Reviews in Control, 36, 220, 10.1016/j.arcontrol.2012.09.004 Qin, 2014, Process data analytics in the era of big data, AIChE Journal, 60, 3092, 10.1002/aic.14523 Qin, 2019, Advances and opportunities in machine learning for process data analytics, Computers & Chemical Engineering, 126, 465, 10.1016/j.compchemeng.2019.04.003 Qin, 2001, On unifying multiblock analysis with application to decentralized process monitoring, Journal of Chemometrics: A Journal of the Chemometrics Society, 15, 715, 10.1002/cem.667 Qin, 2019, Comprehensive process decomposition for closed-loop process monitoring with quality-relevant slow feature analysis, Journal of Process Control, 77, 141, 10.1016/j.jprocont.2019.04.001 Roth, 2008, The group-lasso for generalized linear models: Uniqueness of solutions and efficient algorithms, 848 Shang, 2018 Shang, 2015, Probabilistic slow feature analysis-based representation learning from massive process data for soft sensor modeling, AIChE Journal, 61, 4126, 10.1002/aic.14937 Shang, 2016, Slow feature analysis for monitoring and diagnosis of control performance, Journal of Process Control, 39, 21, 10.1016/j.jprocont.2015.12.004 Shang, 2015, Concurrent monitoring of operating condition deviations and process dynamics anomalies with slow feature analysis, AIChE Journal, 61, 3666, 10.1002/aic.14888 Shang, 2018, Recursive slow feature analysis for adaptive monitoring of industrial processes, IEEE Transactions on Industrial Electronics, 65, 8895, 10.1109/TIE.2018.2811358 Sun, 2017, A sparse reconstruction strategy for online fault diagnosis in nonstationary processes with no a priori fault information, Industrial and Engineering Chemistry Research, 56, 6993, 10.1021/acs.iecr.7b00156 Tibshirani, 1996, Regression shrinkage and selection via the Lasso, Journal of the Royal Statistical Society. Series B. Statistical Methodology, 58, 267 Tulsyan, 2018, Advances in industrial biopharmaceutical batch process monitoring: Machine-learning methods for small data problems, Biotechnology and Bioengineering, 115, 1915, 10.1002/bit.26605 Verhaegen, 2016, N2SID: Nuclear norm subspace identification of innovation models, Automatica, 72, 57, 10.1016/j.automatica.2016.05.021 Westerhuis, 2000, Generalized contribution plots in multivariate statistical process monitoring, Chemometrics and Intelligent Laboratory Systems, 51, 95, 10.1016/S0169-7439(00)00062-9 Wiskott, 2002, Slow feature analysis: Unsupervised learning of invariances, Neural Computation, 14, 715, 10.1162/089976602317318938 Xu, 2013, Weighted reconstruction-based contribution for improved fault diagnosis, Industrial and Engineering Chemistry Research, 52, 9858, 10.1021/ie300679e Yan, 2015, Variable selection method for fault isolation using least absolute shrinkage and selection operator (lasso), Chemometrics and Intelligent Laboratory Systems, 146, 136, 10.1016/j.chemolab.2015.05.019 Yang, 2014 Yang, 2010, Identifying main effects and epistatic interactions from large-scale snp data via adaptive group Lasso, BMC Bioinformatics, 11 Yang, 2015, A fast unified algorithm for solving group-Lasso penalize learning problems, Statistics and Computing, 25, 1129, 10.1007/s11222-014-9498-5 Yin, 2012, A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process, Journal of Process Control, 22, 1567, 10.1016/j.jprocont.2012.06.009 Yin, 2015, Big data for modern industry: Challenges and trends [point of view], Proceedings of the IEEE, 103, 143, 10.1109/JPROC.2015.2388958 Yuan, 2006, Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society. Series B. Statistical Methodology, 68, 49, 10.1111/j.1467-9868.2005.00532.x Zeng, 2015, A Bayesian sparse reconstruction method for fault detection and isolation, Journal of Chemometrics, 29, 349, 10.1002/cem.2712 Zhang, 2019, Bearings fault diagnosis based on adaptive local iterative filtering–multiscale permutation entropy and multinomial logistic model with group-Lasso, Advances in Mechanical Engineering, 11, 1 Zhang, 2009, Decentralized fault diagnosis of large-scale processes using multiblock kernel partial least squares, IEEE Transactions on Industrial Informatics, 6, 3, 10.1109/TII.2009.2033181 Zhao, 2019, Enhanced sparse period-group lasso for bearing fault diagnosis, IEEE Transactions on Industrial Electronics, 66, 2143, 10.1109/TIE.2018.2838070 Zheng, 2019, Extracting dissimilarity of slow feature analysis between normal and different faults for monitoring process status and fault diagnosis, Journal of Chemical Engineering of Japan, 52, 283, 10.1252/jcej.18we079 Zou, 2005, Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society. Series B. Statistical Methodology, 67, 301, 10.1111/j.1467-9868.2005.00503.x Zou, 2006, Sparse principal component analysis, Journal of Computational and Graphical Statistics, 15, 265, 10.1198/106186006X113430