Autonomous Fault Diagnosis and Root Cause Analysis for the Processing System Using One-Class SVM and NN Permutation Algorithm

[1]  Syed Imtiaz,et al.  A deep learning model for process fault prognosis , 2021, Process Safety and Environmental Protection.

[2]  Yuval Cohen,et al.  A smart process controller framework for Industry 4.0 settings , 2021, Journal of Intelligent Manufacturing.

[3]  Syed Imtiaz,et al.  Fault detection and diagnosis in process system using artificial intelligence-based cognitive technique , 2020, Comput. Chem. Eng..

[4]  Yan-Lin He,et al.  Novel Multiblock Transfer Entropy Based Bayesian Network and Its Application to Root Cause Analysis , 2019, Industrial & Engineering Chemistry Research.

[5]  Faisal Khan,et al.  Process system fault detection and diagnosis using a hybrid technique , 2018, Chemical Engineering Science.

[6]  S. Wongsa,et al.  A Robust One -Class Support Vector Machine Using Gaussian -Based Penalty Factor and Its Application to Fault Detection , 2017 .

[7]  Huangang Wang,et al.  Robust one-class SVM for fault detection , 2016 .

[8]  Fan Yang,et al.  Detection of Causality between Process Variables Based on Industrial Alarm Data Using Transfer Entropy , 2015, Entropy.

[9]  J. Oña,et al.  Extracting the contribution of independent variables in neural network models: a new approach to handle instability , 2014, Neural Computing and Applications.

[10]  Huanhuan Chen,et al.  Cognitive fault diagnosis in Tennessee Eastman Process using learning in the model space , 2014, Comput. Chem. Eng..

[11]  Tongwen Chen,et al.  Methods for root cause diagnosis of plant‐wide oscillations , 2014 .

[12]  Tao Chen,et al.  Root cause analysis in multivariate statistical process monitoring: Integrating reconstruction-based multivariate contribution analysis with fuzzy-signed directed graphs , 2014, Comput. Chem. Eng..

[13]  S. Qin,et al.  Root cause diagnosis of plant-wide oscillations using Granger causality , 2014 .

[14]  Sirish L. Shah,et al.  Root cause diagnosis of plant-wide oscillations using the concept of adjacency matrix , 2009 .

[15]  Achim Zeileis,et al.  Conditional variable importance for random forests , 2008, BMC Bioinformatics.

[16]  Nina F. Thornhill,et al.  A continuous stirred tank heater simulation model with applications , 2008 .

[17]  Sirish L. Shah,et al.  Detection and Diagnosis of Plant-wide Oscillations From Industrial Data using the Spectral Envelope Method ? , 2007 .

[18]  Anders L. Madsen,et al.  Applications of object-oriented Bayesian networks for condition monitoring, root cause analysis and decision support on operation of complex continuous processes , 2005, Comput. Chem. Eng..

[19]  Andrew H. Sung,et al.  Ranking importance of input parameters of neural networks , 1998 .

[20]  Kenneth A. Loparo,et al.  A neural-network approach to fault detection and diagnosis in industrial processes , 1997, IEEE Trans. Control. Syst. Technol..

[21]  Venkat Venkatasubramanian,et al.  A neural network methodology for process fault diagnosis , 1989 .

[22]  Krist V. Gernaey,et al.  A novel use for an old problem: The Tennessee Eastman challenge process as an activating teaching tool , 2020 .

[23]  Qing Zhao,et al.  Data-driven root-cause fault diagnosis for multivariate non-linear processes , 2018 .

[24]  James A. Stori,et al.  A Bayesian network approach to root cause diagnosis of process variations , 2005 .

[25]  Josiah C. Hoskins,et al.  Artificial neural network models for knowledge representation in chemical engineering , 1990 .