Process topology convolutional network model for chemical process fault diagnosis

A novel data‐driven methodology for fault detection and dynamic risk assessment

(2020) Md. Tanjin Amin et al.   CANADIAN JOURNAL OF CHEMICAL ENGINEERING

A new unsupervised data mining method based on the stacked autoencoder for chemical process fault diagnosis

(2020) Shaodong Zheng et al.   COMPUTERS & CHEMICAL ENGINEERING

Combining machine learning and process engineering physics towards enhanced accuracy and explainability of data-driven models

(2020) Timur Bikmukhametov et al.   COMPUTERS & CHEMICAL ENGINEERING

Bayesian Network based on Adaptive Threshold Scheme for Fault Detection and Classification

(2020) Chuyue Lou et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Deep fisher autoencoder combined with self-organizing map for visual industrial process monitoring

(2020) Weipeng Lu et al.   JOURNAL OF MANUFACTURING SYSTEMS

Advances and opportunities in machine learning for process data analytics

(2019) S. Joe Qin et al.   COMPUTERS & CHEMICAL ENGINEERING

A novel process monitoring approach based on variational recurrent autoencoder

(2019) Feifan Cheng et al.   COMPUTERS & CHEMICAL ENGINEERING

Fault detection and diagnosis in process system using artificial intelligence-based cognitive technique

(2019) Rajeevan Arunthavanathan et al.   COMPUTERS & CHEMICAL ENGINEERING

Bidirectional Recurrent Neural Network-Based Chemical Process Fault Diagnosis

(2019) Shuyuan Zhang et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Deep convolutional neural network model based chemical process fault diagnosis

(2018) Hao Wu et al.   COMPUTERS & CHEMICAL ENGINEERING

The promise of artificial intelligence in chemical engineering: Is it here, finally?

(2018) Venkat Venkatasubramanian   AICHE JOURNAL

Root Cause Diagnosis of Process Fault Using KPCA and Bayesian Network

(2017) H. Gharahbagheri et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Improved data-based fault detection strategy and application to distillation columns

(2017) Muddu Madakyaru et al.   PROCESS SAFETY AND ENVIRONMENTAL PROTECTION

Abnormal situation management: Challenges and opportunities in the big data era

(2016) Yidan Shu et al.   COMPUTERS & CHEMICAL ENGINEERING

Model selection and overfitting

(2016) Jake Lever et al.   NATURE METHODS

Modified Independent Component Analysis and Bayesian Network-Based Two-Stage Fault Diagnosis of Process Operations

(2015) Hongyang Yu et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Methods and models in process safety and risk management: Past, present and future

(2015) Faisal Khan et al.   PROCESS SAFETY AND ENVIRONMENTAL PROTECTION

Cognitive fault diagnosis in Tennessee Eastman Process using learning in the model space

(2014) Huanhuan Chen et al.   COMPUTERS & CHEMICAL ENGINEERING

Dynamic Risk Assessment and Fault Detection Using Principal Component Analysis

(2012) O. Zadakbar et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process

(2012) Shen Yin et al.   JOURNAL OF PROCESS CONTROL

Fault Diagnosis of Batch Chemical Processes Using a Dynamic Time Warping (DTW)-Based Artificial Immune System

(2011) Yiyang Dai et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

The Graph Neural Network Model

(2008) F. Scarselli et al.   IEEE TRANSACTIONS ON NEURAL NETWORKS