Online monitoring of high-dimensional asynchronous and heterogeneous data streams for shifts in location and scale

References

• Adamo, Francesco, Filippo Attivissimo, Carlo Guarnieri Calo Carducci, and Lanzolla Adamo. 2015. “A Smart Sensor Network for Sea Water Quality Monitoring.” IEEE Sensors Journal 15 (5): 2514–2522. doi:10.1109/JSEN.2014.2360816.
   Web of Science ®Google Scholar
• Ansari, A. R., and R. A. Bradley. 1960. “Rank-Sum Tests for Dispersions.” The Annals of Mathematical Statistics 31 (4): 1174–1189. doi:10.1214/aoms/1177705688.
   Google Scholar
• Aparisi, Francisco, Eugenio K. Epprecht, and Omar Ruiz. 2012. “T2 Control Charts with Variable Dimension.” Journal of Quality Technology 44 (4): 375–393. doi:10.1080/00224065.2012.11917907.
   Web of Science ®Google Scholar
• Bakir, Saad. 2006. “Distribution-Free Quality Control Charts Based on Signed-Rank-Like Statistics.” Communications in Statistics - Theory and Methods 35 (4): 743–757. doi:10.1080/03610920500498907.
   Web of Science ®Google Scholar
• Benjamini, Yoav, and Yosef Hochberg. 1995. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society: Series B (Methodological) 57 (1): 289–300. doi:10.1111/j.2517-6161.1995.tb02031.x.
   Web of Science ®Google Scholar
• Celano, Giovanni, and Subhabrata Chakraborti. 2021. “A Distribution-Free Shewhart-Type Mann–Whitney Control Chart for Monitoring Finite Horizon Productions.” International Journal of Production Research 59 (20): 6069–6086. doi:10.1080/00207543.2020.1802079.
   Web of Science ®Google Scholar
• Chatterjee, Snigdhansu, and Peihua Qiu. 2009. “Distribution-Free Cumulative Sum Control Charts Using Bootstrap-Based Control Limits.” Annals of Applied Statistics 3 (1): 349–369. doi:10.1214/08-AOAS197.
   Web of Science ®Google Scholar
• Cong, Ya, Le Zhou, Zhihuan Song, and Zhiqiang Ge. 2019. “Multirate Dynamic Process Monitoring Based on Multirate Linear Gaussian State-Space Model.” IEEE Transactions on Automation Science and Engineering 16 (4): 1708–1719. doi:10.1109/TASE.2019.2896205.
   Web of Science ®Google Scholar
• Crosier, Ronald B. 1988. “Multivariate Generalizations of Cumulative Sum Quality-Control Schemes.” Technometrics 30 (3): 291–303. doi:10.1080/00401706.1988.10488402.
   Web of Science ®Google Scholar
• Das, Devashish, Shiyu Zhou, Yong Chen, and John Horst. 2016. “Statistical Monitoring of Over-Dispersed Multivariate Count Data Using Approximate Likelihood Ratio Tests.” International Journal of Production Research 54 (21): 6579–6593. doi:10.1080/00207543.2015.1126373.
   Web of Science ®Google Scholar
• Dereszynski, Ethan W., and Thomas G. Dietterich. 2011. “Spatiotemporal Models for Data-Anomaly Detection in Dynamic Environmental Monitoring Campaigns.” ACM Transactions on Sensor Networks 8 (1): 1–36. doi:10.1145/1993042.1993045.
   Web of Science ®Google Scholar
• Ding, Dong, Fugee Tsung, and Jian Li. 2016. “Rank-Based Process Control for Mixed-Type Data.” IIE Transactions 48 (7): 673–683. doi:10.1080/0740817X.2015.1126002.
   Web of Science ®Google Scholar
• Feng, Jian, and Keqin Li. 2020. “MRS-KNN Fault Detection Method for Multirate Sampling Process Based Variable Grouping Threshold.” Journal of Process Control 85: 149–158. doi:10.1016/j.jprocont.2019.11.007.
   Web of Science ®Google Scholar
• Ge, Zhiqiang, and Junghui Chen. 2016. “Plant-Wide Industrial Process Monitoring: A Distributed Modeling Framework.” IEEE Transactions on Industrial Informatics 12 (1): 310–321. doi:10.1109/TII.2015.2509247.
   Web of Science ®Google Scholar
• Girón, Javier, Josep Ginebra, and Alex Riba. 2005. “Bayesian Analysis of a Multinomial Sequence and Homogeneity of Literary Style.” The American Statistician 59 (1): 19–30. doi:10.1198/000313005X21311.
   Web of Science ®Google Scholar
• Goldstein, Markus, and Seiichi Uchida. 2016. “A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data.” PLoS ONE 11 (4): 1–31. doi:10.1371/journal.pone.0152173.
   Web of Science ®Google Scholar
• Graham, Marien A., Amitava Mukherjee, and Subhabrata Chakraborti. 2017. “Design and Implementation Issues for a Class of Distribution-Free Phase II EWMA Exceedance Control Charts.” International Journal of ProductionResearch 55 (8): 2397–2430. doi:10.1080/00207543.2016.1249428.
   Web of Science ®Google Scholar
• Hawkins, Douglas M. 1991. “Multivariate Quality Control Based on Regression-Adiusted Variables.” Technometrics 33 (1): 61–75. doi:10.2307/1269008.
   Web of Science ®Google Scholar
• He, Zhen, Yuan Gao, Liang Qu, and Zhiqiong Wang. 2022. “A Nonparametric CUSUM Scheme for Monitoring Multivariate Time-between-Events-and-Amplitude Data with Application to Automobile Painting.” International Journal of Production Research 60 (18): 5432–5449. doi:10.1080/00207543.2021.1959664.
   Web of Science ®Google Scholar
• Healy, John D. 1987. “A Note on Multivariate CUSUM Procedures.” Technometrics 29 (4): 409–412. doi:10.1080/0040-1706.1987.10488268.
   Web of Science ®Google Scholar
• Hou, Liqun, and Neil W. Bergmann. 2012. “Novel Industrial Wireless Sensor Networks for Machine Condition Monitoring and Fault Diagnosis.” IEEE Transactions on Instrumentation and Measurement 61 (10): 2787–2798. doi:10.1109/TIM.2012.2200817.
   Web of Science ®Google Scholar
• Hou, Shiwang, and Keming Yu. 2021. “A Non-Parametric CUSUM Control Chart for Process Distribution Change Detection and Change Type Diagnosis.” International Journal of Production Research 59 (4): 1166–1186. doi:10.1080/00207543.2020.1721588.
   Web of Science ®Google Scholar
• Huang, Keke, Shujie Wu, Yonggang Li, Chunhua Yang, and Weihua Gui. 2021. “A Multi-Rate Sampling Data Fusion Method for Fault Diagnosis and Its Industrial Applications.” Journal of Process Control 104: 54–61. doi:10.1016/j.jprocont.2021.06.003.
   Web of Science ®Google Scholar
• Huang, Keke, Shujie Wu, Fanbiao Li, Chunhua Yang, and Weihua Gui. 2022. “Fault Diagnosis of Hydraulic Systems Based on Deep Learning Model With Multirate Data Samples.” IEEE Transactions on Neural Networks and Learning Systems 33 (11): 6789–6801. doi:10.1109/TNNLS.2021.3083401.
   PubMed Web of Science ®Google Scholar
• Huerta, Gabriel, Bruno Sansó, and Jonathan R. Stroud. 2004. “A Spatiotemporal Model for Mexico City Ozone Levels.” Journal of the Royal Statistical Society: Series C (Applied Statistics) 53 (2): 231–248. doi:10.1046/j.1467-9876.2003.05100.x.
   Google Scholar
• Jiang, Qingchao, Biao Huang, Steven X. Ding, and Xuefeng Yan. 2016. “Bayesian Fault Diagnosis With Asynchronous Measurements and Its Application in Networked Distributed Monitoring.” IEEE Transactions on Industrial Electronics 63 (10): 6316–6324. doi:10.1109/TIE.2016.2577545.
   Web of Science ®Google Scholar
• Kontar, Raed, Shiyu Zhou, and John Horst. 2017. “Estimation and Monitoring of Key Performance Indicators of Manufacturing Systems Using the Multi-Output Gaussian Process.” International Journal of Production Research 55 (8): 2304–2319. doi:10.1080/00207543.2016.1237791.
   Web of Science ®Google Scholar
• Lee, Joongsup (Jay), Youngmi Hur, Seong-Hee Kim, and James R. Wilson. 2012. “Monitoring Nonlinear Profiles Using a Wavelet-Based Distribution-Free CUSUM Chart.” International Journal of Production Research 50 (22): 6574–6594. doi:10.1080/00207543.2012.655865.
   Web of Science ®Google Scholar
• Leung, Kingsly, and Christopher Leckie. 2005. “Unsupervised Anomaly Detection in Network Intrusion Detection Using Clusters.” In ACSC '05: Proceedings of the Twenty-eighth Australasian conference on Computer Science, Vol. 38, 333–342.
   Google Scholar
• Li, Jun. 2021. “Nonparametric Adaptive CUSUM Chart for Detecting Arbitrary Distributional Changes.” Journal of Quality Technology 53 (2): 154–172. doi:10.1080/00224065.2019.1694398.
   Web of Science ®Google Scholar
• Li, Jian, Kaibo Liu, and Xiaochen Xian. 2017. “Causation-Based Process Monitoring and Diagnosis for Multivariate Categorical Processes.” IISE Transactions 49 (3): 332–343. doi:10.1080/0740817X.2016.1241455.
   Web of Science ®Google Scholar
• Li, Chenglong, Amitava Mukherjee, Qin Su, and Min Xie. 2016. “Optimal Design of a Distribution-Free Quality Control Scheme for Cost-Efficient Monitoring of Unknown Location.” International Journal of Production Research 54 (24): 7259–7273. doi:10.1080/00207543.2016.1173254.
   Web of Science ®Google Scholar
• Li, Zhonghua, and Peihua Qiu. 2014. “Statistical Process Control Using a Dynamic Sampling Scheme.” Technometrics 56 (3): 325–335. doi:10.1080/00401706.2013.844731.
   Web of Science ®Google Scholar
• Li, Wendong, and Peihua Qiu. 2020. “A General Charting Scheme for Monitoring Serially Correlated Data with Short-Memory Dependence and Nonparametric Distributions.” IISE Transactions 52 (1): 61–74. doi:10.1080/24725854.2018.1557794.
   Web of Science ®Google Scholar
• Li, Xue, and Peihua Qiu. 2021. “A Nonparametric CUSUM Chart for Monitoring Multivariate Serially Correlated Processes.” Journal of Quality Technology 53 (4): 396–409. doi:10.1080/00224065.2020.1778430.
   Web of Science ®Google Scholar
• Li, Suyi, LoonChing Tang, and SzuHui Ng. 2010. “Nonparametric CUSUM and EWMA Control Charts for Detecting Mean Shifts.” Journal of Quality Technology 42 (2): 209–226. doi:10.1080/00224065.2010.11917817.
   Web of Science ®Google Scholar
• Li, Wendong, Dongdong Xiang, Fugee Tsung, and Xiaolong Pu. 2020. “A Diagnostic Procedure for High-Dimensional Data Streams via Missed Discovery Rate Control.” Technometrics 62 (1): 84–100. doi:10.1080/00401706.2019.1575284.
   Web of Science ®Google Scholar
• Li, Wendong, Chi Zhang, Tsung Fugee, and Yajun Mei. 2021. “Nonparametric Monitoring of Multivariate Data via KNN Learning.” International Journal of Production Research 59 (20): 6311–6326. doi:10.1080/00207543.2020.1812750.
   Web of Science ®Google Scholar
• Li, Jun, Xin Zhang, and Daniel R. Jeske. 2013. “Nonparametric Multivariate CUSUM Control Charts for Location and Scale Changes.” Journal of Nonparametric Statistics 25 (1): 1–20. doi:10.1080/10485252.2012.726992.
   Web of Science ®Google Scholar
• Liu, Jia Peter, Ran Jin, and Zhenyu James Kong. 2018. “Wafer Quality Monitoring Using Spatial Dirichlet Process Based Mixed-Effect Profile Modeling Scheme.” Journal of Manufacturing Systems 48: 21–32. doi:10.1016/j.jmsy.2018.05.012.
   Web of Science ®Google Scholar
• Liu, Di, Heeseon Kim, Seong-Hee Kim, Taeheung Kim, Dongki Lee, and Yao Xie. 2022. “Distribution-Free Multivariate Time-Series Monitoring with Analytically Determined Control Limits.” International Journal of Production Research, 1–18. doi:10.1080/00207543.2022.2140364.
   Web of Science ®Google Scholar
• Liu, Kaibo, Yajun Mei, and Jianjun Shi. 2015. “An Adaptive Sampling Strategy for Online High-Dimensional Process Monitoring.” Technometrics 57 (3): 305–319. doi:10.1080/00401706.2014.947005.
   Web of Science ®Google Scholar
• Liu, Kaibo, and Jianjun Shi. 2013. “Objective-Oriented Optimal Sensor Allocation Strategy for Process Monitoring and Diagnosis by Multivariate Analysis in a Bayesian Network.” IIE Transactions 45 (6): 630–643. doi:10.1080/0740817X.2012.725505.
   Web of Science ®Google Scholar
• Liu, Kaibo, and Jianjun Shi. 2015. “IoT-Enabled System Informatics for Service Decision Making.” IEEE Intelligent Systems 30 (6): 18–21. doi:10.1109/MIS.2015.111.
   Web of Science ®Google Scholar
• Liu, Liu, Fugee Tsung, and Jian Zhang. 2014. “Adaptive Nonparametric CUSUM Scheme for Detecting Unknown Shifts in Location.” International Journal of Production Research 52 (6): 1592–1606. doi:10.1080/00207543.2013.812260.
   Web of Science ®Google Scholar
• Liu, Kaibo, Xi Zhang, and Jianjun Shi. 2014. “Adaptive Sensor Allocation Strategy for Process Monitoring and Diagnosis in a Bayesian Network.” IEEE Transactions on Automation Science and Engineering 11 (2): 452–462. doi:10.1109/TASE.2013.2287101.
   Web of Science ®Google Scholar
• Lu, B., and Vc Gungor. 2009. “Online and Remote Motor Energy Monitoring and Fault Diagnostics Using Wireless Sensor Networks.” IEEE Transactions on Industrial Electronics 56 (11): 4651–4659. doi:10.1109/TIE.2009.2028349.
   Web of Science ®Google Scholar
• Masuda, Yasuyuki, Hiromasa Kaneko, and Kimito Funatsu. 2014. “Multivariate Statistical Process Control Method Including Soft Sensors for Both Early and Accurate Fault Detection.” Industrial & Engineering Chemistry Research 53 (20): 8553–8564. doi:10.1021/ie501024w.
   Web of Science ®Google Scholar
• Mei, Yajun. 2010. “Efficient Scalable Schemes for Monitoring a Large Number of Data Streams.” Biometrika 97 (2): 419–433. doi:10.1093/biomet/asq010.
   Web of Science ®Google Scholar
• Mei, Yajun. 2011. “Quickest Detection in Censoring Sensor Networks.” In 2011 IEEE International Symposium on Information Theory Proceedings, 2148–2152. St. Petersburg, Russia: IEEE.
   Google Scholar
• Mukherjee, Amitava, and Subha Chakraborti. 2012. “A Distribution-Free Control Chart for the Joint Monitoring of Location and Scale.” Quality and Reliability Engineering International 28 (3): 335–352. doi:10.1002/qre.1249.
   Web of Science ®Google Scholar
• Page, E. S. 1954. “Continuous Inspection Schemes.” Biometrika 41 (1): 100–115. doi:10.1093/biomet/41.1-2.100.
   Web of Science ®Google Scholar
• Perry, Marcus B., and Zhi Wang. 2022. “A Distribution-Free Joint Monitoring Scheme for Location and Scale Using Individual Observations.” Journal of Quality Technology 54 (2): 144–161. doi:10.1080/00224065.2020.1829213.
   Web of Science ®Google Scholar
• Pignatiello, Joseph J., and George C. Runger. 1990. “Comparisons of Multivariate CUSUM Charts.” Journal of Quality Technology 22 (3): 173–186. doi:10.1080/00224065.1990.11979237.
   Web of Science ®Google Scholar
• Qiu, Peihua. 2008. “Distribution-Free Multivariate Process Control Based on Log-Linear Modeling.” IIE Transactions 40 (7): 664–677. doi:10.1080/07408170701744843.
   Web of Science ®Google Scholar
• Qiu, Peihua. 2018. “Some Perspectives on Nonparametric Statistical Process Control.” Journal of Quality Technology 50 (1): 49–65. doi:10.1080/00224065.2018.1404315.
   Web of Science ®Google Scholar
• Qiu, Peihua, and D. Hawkins. 2001. “A Rank-Based Multivariate CUSUM Procedure.” Technometrics 43 (2): 120–132. doi:10.1198/004017001750386242.
   Web of Science ®Google Scholar
• Qiu, Peihua, Zhen He, and Zhiqiong Wang. 2019. “Nonparametric Monitoring of Multiple Count Data.” IISE Transactions 51 (9): 972–984. doi:10.1080/24725854.2018.1530486.
   Web of Science ®Google Scholar
• Qiu, Peihua, and Zhonghua Li. 2011. “On Nonparametric Statistical Process Control of Univariate Processes.” Technometrics 53 (4): 390–405. doi:10.1198/TECH.2011.10005.
   Web of Science ®Google Scholar
• Qiu, Peihua, and Kai Yang. 2022. “Spatio-Temporal Process Monitoring Using Exponentially Weighted Spatial LASSO.” Journal of Quality Technology, 1–18. doi:10.1080/00224065.2022.2081104.
   Web of Science ®Google Scholar
• Rieth, Cory A., Ben D. Amsel, Randy Tran, and Maia B. Cook. 2017. “Additional Tennessee Eastman Process Simulation Data for Anomaly Detection Evaluation.” Harvard Dataverse v1.
   Google Scholar
• Shi, Jianjun. 2023. “In-Process Quality Improvement: Concepts, Methodologies, and Applications.” IISE Transactions 55 (1): 2–21. doi:10.1080/24725854.2022.2059725.
   Web of Science ®Google Scholar
• Tartakovsky, Alexander G., Boris L. Rozovskii, Rudolf B. Blažek, and Hongjoong Kim. 2006. “Detection of Intrusions in Information Systems by Sequential Change-Point Methods.” Statistical Methodology 3 (3): 252–293. doi:10.1016/j.stamet.2005.05.003.
   Google Scholar
• Wang, Jingyi, Yousef Alipouri, and Biao Huang. 2021. “Multirate Sensor Fusion in the Presence of Irregular Measurements and Time-Varying Time Delays Using Synchronized, Neural, Extended Kalman Filters.” IEEE Transactions on Instrumentation and Measurement 71: 2500509. doi:10.1109/TIM.2021.3135537.
   Web of Science ®Google Scholar
• Wang, Di, Fangyu Li, and Kaibo Liu. 2023. “Modeling and Monitoring of a Multivariate Spatio-Temporal Network System.” IISE Transactions 55 (4): 331–347. doi:10.1080/24725-854.2021.1973157.
   Web of Science ®Google Scholar
• Wang, Andi, Xiaochen Xian, Fugee Tsung, and Kaibo Liu. 2018. “A Spatial-Adaptive Sampling Procedure for Online Monitoring of Big Data Streams.” Journal of Quality Technology 50 (4): 329–343. doi:10.1080/00224065.2018.1507560.
   Web of Science ®Google Scholar
• Wang, Chao, and Shiyu Zhou. 2017. “Contamination Source Identification Based on Sequential Bayesian Approach for Water Distribution Network with Stochastic Demands.” IISE Transactions 49 (9): 899–910. doi:10.1080/24725854.2017.1315782.
   Web of Science ®Google Scholar
• Wu, Yao, and Xionglin Luo. 2010. “A Novel Calibration Approach of Soft Sensor Based on Multirate Data Fusion Technology.” Journal of Process Control 20 (10): 1252–1260. doi:10.1016/j.jprocont.2010.09.003.
   Web of Science ®Google Scholar
• Xian, Xiaochen, Jian Li, and Kaibo Liu. 2019. “Causation-Based Monitoring and Diagnosis for Multivariate Categorical Processes with Ordinal Information.” IEEE Transactions on Automation Science and Engineering 16 (2): 886–897. doi:10.1109/TASE.2018.2873365.
   Web of Science ®Google Scholar
• Xian, Xiaochen, Andi Wang, and Kaibo Liu. 2018. “A Nonparametric Adaptive Sampling Strategy for Online Monitoring of Big Data Streams.” Technometrics 60 (1): 14–25. doi:10.1080/00401706.2017.1317291.
   Web of Science ®Google Scholar
• Ye, Honghan, and Kaibo Liu. 2022. “A Generic Online Nonparametric Monitoring and Sampling Strategy for High-Dimensional Heterogeneous Processes.” IEEE Transactions on Automation Science and Engineering 19 (3): 1503–1516. doi:10.1109/TASE.2022.3146391.
   Web of Science ®Google Scholar
• Ye, Honghan, Xiaochen Xian, Jing-Ru C. Cheng, Brock Hable, Shannon Robert, Mojtaba Kadkhodaie Elyaderani, and Kaibo Liu. 2023. “Online Nonparametric Monitoring of Heterogeneous Data Streams with Partial Observations Based on Thompson Sampling.” IISE Transactions 55 (4): 392–404. doi:10.1080/24725854.2022.2039423.
   Web of Science ®Google Scholar
• Yeh, Arthur B., Bo Li, and Kaibo Wang. 2012. “Monitoring Multivariate Process Variability with Individual Observations via Penalised Likelihood Estimation.” International Journal of Production Research 50 (22): 6624–6638. doi:10.1080/00207543.2012.676684.
   Web of Science ®Google Scholar
• Yin, Shen, Steven X. Ding, Adel Haghani, Haiyang Hao, and Ping Zhang. 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 (9): 1567–1581. doi:10.1016/j.jprocont.2012.06.009.
   Web of Science ®Google Scholar
• Zhou, Le, Junghui Chen, Jing Jie, and Zhihuan Song. 2019. “Multiple Probability Principal Component Analysis for Process Monitoring with Multi-Rate Measurements.” Journal of the Taiwan Institute of Chemical Engineers 96: 18–28. doi:10.1016/j.jtice.2018.11.002.
   Web of Science ®Google Scholar
• Zhou, Le, Junghui Chen, Zhihuan Song, and Zhiqiang Ge. 2015. “Semi-Supervised PLVR Models for Process Monitoring with Unequal Sample Sizes of Process Variables and Quality Variables.” Journal of Process Control 26: 1–16. doi:10.1016/j.jprocont.2014.11.013.
   Web of Science ®Google Scholar
• Zhou, Le, Yaoxin Wang, Zhiqiang Ge, and Zhihuan Song. 2019. “Multirate Factor Analysis Models for Fault Detection in Multirate Processes.” IEEE Transactions on Industrial Informatics 15 (7): 4076–4085. doi:10.1109/TII.2018.2889750.
   Web of Science ®Google Scholar
• Zou, Changliang, and Fugee Tsung. 2011. “A Multivariate Sign EWMA Control Chart.” Technometrics 53 (1): 84–97. doi:10.1198/TECH.2010.09095.
   Web of Science ®Google Scholar
• Zou, Changliang, Zhaojun Wang, Xuemin Zi, and Wei Jiang. 2015. “An Efficient Online Monitoring Method for High-Dimensional Data Streams.” Technometrics 57 (3): 374–387. doi:10.1080/00401706.2014.940089.
   Web of Science ®Google Scholar