Double-weighted neighborhood standardization method with applications to multimode-process fault detection

[1]  Xue Z. Wang,et al.  Making use of process tomography data for multivariate statistical process control , 2011 .

[2]  S. Joe Qin,et al.  Statistical process monitoring: basics and beyond , 2003 .

[3]  Yingwei Zhang,et al.  Modeling and monitoring of multimode process based on subspace separation , 2013 .

[4]  Rongrong Sun,et al.  Fault diagnosis with between mode similarity analysis reconstruction for multimode processes , 2017 .

[5]  S. Qin,et al.  Multimode process monitoring with Bayesian inference‐based finite Gaussian mixture models , 2008 .

[6]  Jie Zhang,et al.  Performance monitoring of processes with multiple operating modes through multiple PLS models , 2006 .

[7]  David J. Sandoz,et al.  The application of principal component analysis and kernel density estimation to enhance process monitoring , 2000 .

[8]  Steven X. Ding,et al.  A Review on Basic Data-Driven Approaches for Industrial Process Monitoring , 2014, IEEE Transactions on Industrial Electronics.

[9]  Linxia Liao,et al.  Discovering Prognostic Features Using Genetic Programming in Remaining Useful Life Prediction , 2014, IEEE Transactions on Industrial Electronics.

[10]  Wu Deng,et al.  Fault Diagnosis Method Based on Principal Component Analysis and Broad Learning System , 2019, IEEE Access.

[11]  Gérard-André Capolino,et al.  Modern Diagnostics Techniques for Electrical Machines, Power Electronics, and Drives , 2015, IEEE Transactions on Industrial Electronics.

[12]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[13]  Zhiqiang Ge,et al.  Two-dimensional Bayesian monitoring method for nonlinear multimode processes , 2011 .

[14]  Tahir Mehmood,et al.  A review of variable selection methods in Partial Least Squares Regression , 2012 .

[15]  Jian Hou,et al.  An Improved Principal Component Regression for Quality-Related Process Monitoring of Industrial Control Systems , 2017, IEEE Access.

[16]  Huijun Gao,et al.  Data-Based Techniques Focused on Modern Industry: An Overview , 2015, IEEE Transactions on Industrial Electronics.

[17]  Yuan Yao,et al.  Statistical analysis and online monitoring for multimode processes with between-mode transitions , 2010 .

[18]  Yi Hu,et al.  A novel local neighborhood standardization strategy and its application in fault detection of multimode processes , 2012 .

[19]  Furong Gao,et al.  Review of Recent Research on Data-Based Process Monitoring , 2013 .

[20]  Zhiqiang Ge,et al.  Robust Online Monitoring for Multimode Processes Based on Nonlinear External Analysis , 2008 .

[21]  Danwei Wang,et al.  Model-Based Prognosis for Hybrid Systems With Mode-Dependent Degradation Behaviors , 2014, IEEE Transactions on Industrial Electronics.

[22]  Bo Zhao,et al.  Process Monitoring via Key Principal Components and Local Information Based Weights , 2019, IEEE Access.

[23]  Jin Wang,et al.  Fault Detection Using the k-Nearest Neighbor Rule for Semiconductor Manufacturing Processes , 2007, IEEE Transactions on Semiconductor Manufacturing.

[24]  In-Beum Lee,et al.  Multivariate Nonlinear Statistical Process Control of a Sequencing Batch Reactor , 2006 .

[25]  Tao Chen,et al.  On-line multivariate statistical monitoring of batch processes using Gaussian mixture model , 2010, Comput. Chem. Eng..

[26]  Nan Li,et al.  Statistical process monitoring based on modified nonnegative matrix factorization , 2015, J. Intell. Fuzzy Syst..

[27]  Yan Zhang,et al.  On the Euclidean distance of images , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Guang Wang,et al.  Quality-Related Fault Detection Approach Based on Orthogonal Signal Correction and Modified PLS , 2015, IEEE Transactions on Industrial Informatics.

[29]  Rajagopalan Srinivasan,et al.  Multi-model based process condition monitoring of offshore oil and gas production process , 2010 .

[30]  S. Zhao,et al.  Monitoring of Processes with Multiple Operating Modes through Multiple Principle Component Analysis Models , 2004 .

[31]  Shen Yin,et al.  Real-Time Monitoring and Control of Industrial Cyberphysical Systems: With Integrated Plant-Wide Monitoring and Control Framework , 2019, IEEE Industrial Electronics Magazine.

[32]  Jianbo Yu,et al.  Hidden Markov models combining local and global information for nonlinear and multimodal process monitoring , 2010 .

[33]  Keqin Li,et al.  MRS-kNN fault detection method for multirate sampling process based variable grouping threshold , 2020 .

[34]  Shen Yin,et al.  Recent Advances in Key-Performance-Indicator Oriented Prognosis and Diagnosis With a MATLAB Toolbox: DB-KIT , 2019, IEEE Transactions on Industrial Informatics.

[35]  Shuai Li,et al.  Dynamical process monitoring using dynamical hierarchical kernel partial least squares , 2012 .