Fault diagnosis of chemical processes based on joint recurrence quantification analysis

[1]  Navid Mostoufi,et al.  Joint recurrence based root cause analysis of nonlinear multivariate chemical processes , 2021, Journal of Process Control.

[2]  Krist V. Gernaey,et al.  Integration of first-principle models and machine learning in a modeling framework: An application to flocculation , 2021 .

[3]  Seyed Soheil Mansouri,et al.  Data-Driven Fault Diagnosis of Chemical Processes Based on Recurrence Plots , 2021 .

[4]  Hongbo Shi,et al.  Information concentrated variational auto-encoder for quality-related nonlinear process monitoring , 2020 .

[5]  Krist V. Gernaey,et al.  Hybrid machine learning assisted modelling framework for particle processes , 2020, Comput. Chem. Eng..

[6]  Xiaofeng Yuan,et al.  LDA-based deep transfer learning for fault diagnosis in industrial chemical processes , 2020, Comput. Chem. Eng..

[7]  Hazem Nounou,et al.  Fault diagnosis of biological systems using improved machine learning technique , 2020, International Journal of Machine Learning and Cybernetics.

[8]  Jakob Kjøbsted Huusom,et al.  A mass and energy balance stage model for cyclic distillation , 2020 .

[9]  Weihua Gui,et al.  Deep learning for fault-relevant feature extraction and fault classification with stacked supervised auto-encoder , 2020 .

[10]  Rafiqul Gani,et al.  ProCACD: A computer-aided versatile tool for process control , 2020, Comput. Chem. Eng..

[11]  James R. Ottewill,et al.  An on-line framework for monitoring nonlinear processes with multiple operating modes , 2020 .

[12]  Chang Peng,et al.  Fault diagnosis of microbial pharmaceutical fermentation process with non-Gaussian and nonlinear coexistence , 2020 .

[13]  Kevin Van Geem,et al.  A multi-layered view of chemical and biochemical engineering , 2020 .

[14]  Shijin Wang,et al.  One-dimensional convolutional auto-encoder-based feature learning for fault diagnosis of multivariate processes , 2020 .

[15]  Yong Zeng,et al.  Fault diagnosis based on variable-weighted separability-oriented subclass discriminant analysis , 2019, Comput. Chem. Eng..

[16]  Clayton R. Pereira,et al.  A recurrence plot-based approach for Parkinson's disease identification , 2019, Future Gener. Comput. Syst..

[17]  Mohd Azlan Hussain,et al.  A review of data-driven fault detection and diagnosis methods: applications in chemical process systems , 2019 .

[18]  N. Mostoufi,et al.  Recognition of Particle Size Changes in Fluidized Beds by Recurrence and Cross Recurrence Quantification Analyses , 2018, Industrial & Engineering Chemistry Research.

[19]  Funa Zhou,et al.  Fault diagnosis based on deep learning subject to missing data , 2018, 2018 Chinese Control And Decision Conference (CCDC).

[20]  Biao Huang,et al.  A novel approach to process operating mode diagnosis using conditional random fields in the presence of missing data , 2018, Comput. Chem. Eng..

[21]  Zhiqiang Ge,et al.  Review on data-driven modeling and monitoring for plant-wide industrial processes , 2017 .

[22]  Chin-Teng Lin,et al.  A review of clustering techniques and developments , 2017, Neurocomputing.

[23]  Xunyuan Yin,et al.  Distributed output‐feedback fault detection and isolation of cascade process networks , 2017 .

[24]  Richard D. Braatz,et al.  Principal Component Analysis of Process Datasets with Missing Values , 2017 .

[25]  Bo Lu,et al.  Big Data Analytics in Chemical Engineering. , 2017, Annual review of chemical and biomolecular engineering.

[26]  N. Mostoufi,et al.  Investigation of hydrodynamics of gas-solid fluidized beds using cross recurrence quantification analysis , 2017 .

[27]  Jose A. Romagnoli,et al.  Data mining and clustering in chemical process databases for monitoring and knowledge discovery , 2017, Journal of Process Control.

[28]  N. Mostoufi,et al.  University of Birmingham Non-intrusive characterization of particle size changes in fluidized beds using recurrence plots , 2016 .

[29]  Moisès Graells,et al.  Fault diagnosis of chemical processes with incomplete observations: A comparative study , 2016, Comput. Chem. Eng..

[30]  Enrico Zio,et al.  Clustering for unsupervised fault diagnosis in nuclear turbine shut-down transients , 2015 .

[31]  Navid Mostoufi,et al.  Characterization of fluidized beds hydrodynamics by recurrence quantification analysis and wavelet transform , 2015 .

[32]  Fei Liu,et al.  Fault Detection and Diagnosis of Multiple-Model Systems With Mismodeled Transition Probabilities , 2015, IEEE Transactions on Industrial Electronics.

[33]  Biao Huang,et al.  Expectation–Maximization Approach to Fault Diagnosis With Missing Data , 2015, IEEE Transactions on Industrial Electronics.

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

[35]  Juan Adánez,et al.  Progress in chemical-looping combustion and reforming technologies , 2012 .

[36]  Zhiqiang Ge,et al.  Kernel Generalization of PPCA for Nonlinear Probabilistic Monitoring , 2010 .

[37]  Lingbo Yu,et al.  Probabilistic principal component analysis with expectation maximization (PPCA-EM) facilitates volume classification and estimates the missing data. , 2010, Journal of structural biology.

[38]  Aníbal R. Figueiras-Vidal,et al.  Pattern classification with missing data: a review , 2010, Neural Computing and Applications.

[39]  J. Romagnoli,et al.  A Fault Detection and Diagnosis Strategy for Batch/Semi-Batch Processes , 2010 .

[40]  Jürgen Kurths,et al.  Recurrence plots for the analysis of complex systems , 2009 .

[41]  John Gregory,et al.  Monitoring particle aggregation processes. , 2009, Advances in colloid and interface science.

[42]  Anil K. Jain.  Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..

[43]  Julia Hirschberg,et al.  V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure , 2007, EMNLP.

[44]  M. Potkonjak,et al.  Markov chain-based models for missing and faulty data in MICA2 sensor motes , 2005, IEEE Sensors, 2005..

[45]  Sudipto Guha,et al.  CURE: an efficient clustering algorithm for large databases , 1998, SIGMOD '98.

[46]  P. A. Taylor,et al.  Missing data methods in PCA and PLS: Score calculations with incomplete observations , 1996 .

[47]  E. F. Vogel,et al.  A plant-wide industrial process control problem , 1993 .

[48]  D. Ruelle,et al.  Recurrence Plots of Dynamical Systems , 1987 .

[49]  Krist V. Gernaey,et al.  Integration of Computational Chemistry and Artificial Intelligence for Multi-scale Modeling of Bioprocesses , 2020, Computer Aided Chemical Engineering.

[50]  R. Mosdorf,et al.  Identifying synchronization between flow boiling inside two parallel minichannels using joint recurrence plots , 2018 .

[51]  Zhiqiang Ge,et al.  Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data , 2018, Annu. Rev. Control..

[52]  Jan Van Impe,et al.  Feature extraction for batch process monitoring and fault detection via simultaneous data scaling and training of tensor based models , 2018 .

[53]  Sylvain Verron,et al.  Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges , 2016, Annu. Rev. Control..

[54]  Rafiqul Gani,et al.  Integrated Process Design and Control of Multi-element Reactive Distillation Processes , 2016 .

[55]  L. Hubert,et al.  Comparing partitions , 1985 .