[HTML][HTML] A review on autoencoder based representation learning for fault detection and diagnosis in industrial processes
Process monitoring technologies play a key role in maintaining the steady state of industrial
processes. However, with the increasing complexity of modern industrial processes …
processes. However, with the increasing complexity of modern industrial processes …
Data‐driven modelling methods in sintering process: Current research status and perspectives
The sintering process, as a primary modus of the blast furnace ironmaking industry, has
enormous economic value and environmental protection significance for the iron and steel …
enormous economic value and environmental protection significance for the iron and steel …
Prediction of multiple molten iron quality indices in the blast furnace ironmaking process based on attention-wise deep transfer network
Molten iron quality (MIQ) indices prediction based on data-driven models is an important
way to monitor product quality and smelting status in the blast furnace ironmaking process …
way to monitor product quality and smelting status in the blast furnace ironmaking process …
Blast furnace ironmaking process monitoring with time-constrained global and local nonlinear analytic stationary subspace analysis
In this article, a novel time-constrained global and local nonlinear analytic stationary
subspace analysis (Tc-GLNASSA) is proposed to enhance blast furnace ironmaking process …
subspace analysis (Tc-GLNASSA) is proposed to enhance blast furnace ironmaking process …
Adaptive dynamic inferential analytic stationary subspace analysis: A novel method for fault detection in blast furnace ironmaking process
Detecting faults in blast furnace ironmaking process (BFIP) remains a challenging task due
to the hybrid properties involving dynamics and nonstationarity. To address this problem …
to the hybrid properties involving dynamics and nonstationarity. To address this problem …
基于动态注意力深度迁移网络的高炉铁水硅含量在线预测方法
蒋珂， 蒋朝辉， 谢永芳， 潘冬， 桂卫华 - 自动化学报, 2023 - aas.net.cn
铁水硅含量是反映高炉冶炼过程中热状态变化的灵敏指示剂, 但无法实时在线检测,
造成铁水质量调控盲目. 为此, 提出一种基于动态注意力深度迁移网络(Attention deep transfer …
造成铁水质量调控盲目. 为此, 提出一种基于动态注意力深度迁移网络(Attention deep transfer …
Variational Discriminative Stacked Auto-Encoder: Feature Representation Using a Prelearned Discriminator, and Its Application to Industrial Process Monitoring
J Huang, X Sun, SX Ding, X Yang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
In deep-learning-based process monitoring, obtaining an effective feature representation is
a critical step in constructing a reliable deep-learning monitoring model. Conventional deep …
a critical step in constructing a reliable deep-learning monitoring model. Conventional deep …
Reinforcement learning for blast furnace ironmaking operation with safety and partial observation considerations
Making proper decision online in complex environment during the blast furnace (BF)
operation is a key factor in achieving long-term success and profitability in the steel …
operation is a key factor in achieving long-term success and profitability in the steel …
A novel intelligent monitoring method for the closing time of the taphole of blast furnace based on two-stage classification
Determining the taphole closing time is an essential task in the blast furnace ironmaking
process because the closing time directly affects the efficiency of iron production and the …
process because the closing time directly affects the efficiency of iron production and the …
A Fault Identification Method for Electric Submersible Pumps Based on DAE‐SVM
P Yang, J Chen, H Zhang, S Li - Shock and Vibration, 2022 - Wiley Online Library
The purpose of this study was to investigate how to detect abnormalities in electric
submersible pumps (ESPs) in advance and how to classify the faults by monitoring the …
submersible pumps (ESPs) in advance and how to classify the faults by monitoring the …