Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
[HTML][HTML] Physics-informed probabilistic slow feature analysis
This paper presents a novel approach called physics-informed probabilistic slow feature
analysis. The probabilistic slow feature analysis method has been employed to extract …
analysis. The probabilistic slow feature analysis method has been employed to extract …
Active fault diagnosis for uncertain LPV systems: A zonotopic set-membership approach
Z Zhang, X He, D Zhou - IEEE Transactions on Automation …, 2023 - ieeexplore.ieee.org
Active fault diagnosis (AFD) techniques can improve fault diagnosis performance by
designing a set of appropriate auxiliary inputs and injecting them into the system to stimulate …
designing a set of appropriate auxiliary inputs and injecting them into the system to stimulate …
Hybrid probabilistic slow feature analysis of continuous and binary data for dynamic process monitoring
Industrial process data are usually high-dimensional with dynamic characteristics, and a mix
of continuous and binary quantities. However, current dynamic latent variable (DLV) …
of continuous and binary quantities. However, current dynamic latent variable (DLV) …
Sparse robust dynamic feature extraction using Bayesian inference
Datasets of large-scale industrial processes are often high-dimensional and are
characterized by outliers. Probabilistic latent variable models are effective for modeling such …
characterized by outliers. Probabilistic latent variable models are effective for modeling such …
Full condition monitoring of geological drilling process based on just-in-time learning-aided slow feature analysis
Presently, the demand for precise process monitoring during geological drilling has
increased dramatically. However, there exists complex dynamic characteristics due to the …
increased dramatically. However, there exists complex dynamic characteristics due to the …
A fault detection method based on the dynamic k-nearest neighbor model and dual control chart
L Liu, J Liu, H Wang, S Tan, Y Liu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The incipient fault detection of a complex industrial process is a challenging problem for
traditional dynamic detection methods. Traditional dynamic detection methods usually …
traditional dynamic detection methods. Traditional dynamic detection methods usually …
Soft Sensor Enhancement for Multimodal Industrial Process Data: Meta Regression Gaussian Mixture Variational Autoencoder
Traditional industrial soft sensors often treat industrial process data as uniformly distributed
or unimodal. However, in reality, due to variations in operating conditions, industrial process …
or unimodal. However, in reality, due to variations in operating conditions, industrial process …
Multiscale kernel entropy component analysis with application to complex industrial process monitoring
P Xu, J Liu, W Zhang, H Wang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Modern industrial processes are characterized by numerous measurement points and wide
operating ranges, resulting in extremely complex correlations among variables. Therefore …
operating ranges, resulting in extremely complex correlations among variables. Therefore …
Fault detection of multimode chemical processes using weighted density peak clustering and trend slow feature analysis
X Deng, M Wu, W Yang, X Tang, Y Cao - Process Safety and …, 2025 - Elsevier
Modern chemical processes frequently operate under various modes due to the alterations
of raw materials and market demands. For monitoring faults in multimode chemical …
of raw materials and market demands. For monitoring faults in multimode chemical …
Hybrid Input–Output Probabilistic Slow Feature Analysis for adaptive process monitoring
Industrial process data are usually dynamic due to closed-loop control systems. Current
dynamic latent-variable methods generally assume that the dynamics of the process are …
dynamic latent-variable methods generally assume that the dynamics of the process are …