Abnormal Situation Management in Chemical Processes: Recent Research Progress and Future Prospects
S Liu, F Lei, D Zhao, Q Liu - Processes, 2023 - mdpi.com
In the chemical process, abnormal situations are precursor events of incidents and
accidents. Abnormal situation management (ASM) can effectively identify abnormalities and …
accidents. Abnormal situation management (ASM) can effectively identify abnormalities and …
A spatial–temporal variational graph attention autoencoder using interactive information for fault detection in complex industrial processes
Modern industry processes are typically composed of multiple operating units with reaction
interaction and energy–mass coupling, which result in a mixed time-varying and spatial …
interaction and energy–mass coupling, which result in a mixed time-varying and spatial …
Dual-attention LSTM autoencoder for fault detection in industrial complex dynamic processes
Complex dynamic characteristics resulting from multi-system coupling and closed-loop
control are ubiquitous in modern industrial process data, presenting significant challenges …
control are ubiquitous in modern industrial process data, presenting significant challenges …
Graph neural networks for anomaly detection and diagnosis in hydrogen extraction systems
Recent research has been actively conducted on fault diagnosis in hydrogen extraction
systems using artificial intelligence. However, existing studies have not considered the …
systems using artificial intelligence. However, existing studies have not considered the …
Fault detection using Graph Neural Differential Auto-encoders (GNDAE)
In this study, we propose a Graph neural Differential Auto-encoder (GNDAE) model for fault
detection and process monitoring. The GNDAE framework is capable of dealing with graph …
detection and process monitoring. The GNDAE framework is capable of dealing with graph …
Explainable AI methodology for understanding fault detection results during Multi-Mode operations
Multi-mode operations are prevalent in the chemical industry. Various methods have been
proposed for monitoring multi-mode operations. Of these, AI-based approaches such as …
proposed for monitoring multi-mode operations. Of these, AI-based approaches such as …
IC points weight learning-based GCN and improving feature distribution for industrial fault diagnosis
Industrial fault diagnosis (FD) is crucial to detect the causes of faults in a timely manner and
solve the problems to maintain stable production. Adequately considering the spatial …
solve the problems to maintain stable production. Adequately considering the spatial …
Spatial-temporal associations representation and application for process monitoring using graph convolution neural network
Modern industrial processes generate many dynamic, associated, and multi-scale variables,
which are more likely to implicit spatial-temporal associations knowledge for describing …
which are more likely to implicit spatial-temporal associations knowledge for describing …
A knowledge-driven spatial-temporal graph neural network for quality-related fault detection
L Guo, H Shi, S Tan, B Song, Y Tao - Process Safety and Environmental …, 2024 - Elsevier
The majority of quality-related fault detection methods focused on process statistics,
neglecting the spatial-temporal characteristics of variables and the physical information of …
neglecting the spatial-temporal characteristics of variables and the physical information of …
Fault detection and identification method: 3D-CNN combined with continuous wavelet transform
C Ukawa, Y Yamashita - Computers & Chemical Engineering, 2024 - Elsevier
This study proposes a novel fault detection and identification method using Continuous
Wavelet Transform (CWT) and a three-dimensional Convolutional Neural Network (3D …
Wavelet Transform (CWT) and a three-dimensional Convolutional Neural Network (3D …