Fault detection and isolation using probabilistic wavelet neural operator auto-encoder with application to dynamic processes
Fault detection and isolation are crucial aspects that need to be considered for the safe and
reliable operation of process systems. The modern industrial process frequently employs …
reliable operation of process systems. The modern industrial process frequently employs …
Kernel-based statistical process monitoring and fault detection in the presence of missing data
Missing data widely exist in industrial processes and lead to difficulties in modeling,
monitoring, fault diagnosis, and control. In this article, we propose a nonlinear method to …
monitoring, fault diagnosis, and control. In this article, we propose a nonlinear method to …
A multivariate monitoring method based on kernel principal component analysis and dual control chart
L Liu, J Liu, H Wang, S Tan, M Yu, P Xu - Journal of Process Control, 2023 - Elsevier
The kernel principal component analysis (KPCA) method has been widely applied in
process monitoring. However, the KPCA method still has two challenging problems that …
process monitoring. However, the KPCA method still has two challenging problems that …
A local dynamic broad kernel stationary subspace analysis for monitoring blast furnace ironmaking process
For the actual blast furnace ironmaking process (BFIP), sophisticated dynamic, nonlinear,
and nonstationary characteristics make it hard to be modeled accurately with conventional …
and nonstationary characteristics make it hard to be modeled accurately with conventional …
A TOPSIS-based relocalization algorithm in wireless sensor networks
Selecting reliable beacon nodes plays a significant role in relocalizing unknown nodes in a
wireless sensor network. When the position of a beacon node is drifted or is spoofed, it …
wireless sensor network. When the position of a beacon node is drifted or is spoofed, it …
Incipient fault diagnosis and trend prediction in nonlinear closed-loop systems with Gaussian and non-Gaussian noise
This paper proposes a methodology for incipient fault diagnosis and the corresponding
trend prediction in nonlinear closed-loop systems considering stochastic Gaussian and non …
trend prediction in nonlinear closed-loop systems considering stochastic Gaussian and non …
Structured fault information-aided canonical variate analysis model for dynamic process monitoring
Process monitoring is one of the most crucial fundamental components in industrial
processes. Traditional multivariate statistical analysis modeling only relies on data collected …
processes. Traditional multivariate statistical analysis modeling only relies on data collected …
数据驱动的燃煤发电装备运行工况监控——现状与展望
赵春晖， 胡赟昀， 郑嘉乐， 陈军豪 - 自动化学报, 2022 - aas.net.cn
大容量, 高参数, 低能耗的百万千瓦超超临界机组是燃煤发电领域的重大装备,
已成为全国电力工业发展的主流方向, 其安全可靠运行对推动发电企业转型升级具有重要意义 …
已成为全国电力工业发展的主流方向, 其安全可靠运行对推动发电企业转型升级具有重要意义 …
Incipient fault detection for dynamic chemical processes based on enhanced CVDA integrated with probability information and fault-sensitive features
X Deng, X Liu, Y Cao, L Cong, Z Li - Journal of Process Control, 2022 - Elsevier
Recently, canonical variate dissimilarity analysis (CVDA) has emerged as an efficient
incipient fault monitoring method for dynamic chemical processes. Nevertheless, the basic …
incipient fault monitoring method for dynamic chemical processes. Nevertheless, the basic …
Time-series transfer learning: An early stage imbalance fault detection method based on feature enhancement and improved support vector data description
X Ni, D Yang, H Zhang, F Qu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Early stage fault detection plays a pivotal role in Industrial equipment accidents avoidance
and scientific maintenance. While limited by the complex operation background, its …
and scientific maintenance. While limited by the complex operation background, its …