Xin Ma

Orcid: 0000-0003-1291-3977

Affiliations:
  • Beijing University of Chemical Technology, Beijing, China


According to our database1, Xin Ma authored at least 23 papers between 2019 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

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Online presence:

On csauthors.net:

Bibliography

2026
Monitoring and Fault Risk Analysis for Nonlinear Dynamic Processes Based on Kernel Dynamic Regression Model.
IEEE Trans. Control. Syst. Technol., January, 2026

Intelligent fault diagnosis in hybrid chemical processes under limited samples based on multi-feature fusion learning.
Comput. Chem. Eng., 2026

2025
Multimode Monitoring Method Based on Adaptive Importance Coding Dictionary Learning.
IEEE Trans. Reliab., December, 2025

A Dynamic Process Modeling Method Based on Bipartite Graph and Recursive Monitoring for Catalytic Cracking Unit.
IEEE Trans. Control. Syst. Technol., November, 2025

BBATProt: a framework predicting biological function with enhanced feature extraction via interpretable deep learning.
Briefings Bioinform., November, 2025

Jarque-Bera-Based Artificial Neural Correlation Analysis for Nonlinear and Non-Gaussian Process Monitoring.
IEEE Trans. Syst. Man Cybern. Syst., September, 2025

Globality Meets Locality: An Anchor Graph Collaborative Learning Framework for Fast Multiview Subspace Clustering.
IEEE Trans. Neural Networks Learn. Syst., June, 2025

Fast Sparse Dynamic Matrix Estimation Method With Differential Information for Industrial Process Monitoring.
IEEE Trans. Control. Syst. Technol., March, 2025

Dynamic Process Monitoring Based on Dot Product Feature Analysis for Thermal Power Plants.
IEEE CAA J. Autom. Sinica, March, 2025

FIGNN: Fuzzy Inference-Guided Graph Neural Network for Fault Diagnosis in Industrial Processes.
IEEE Trans. Instrum. Meas., 2025

Spatiotemporal Local Analysis for Nonlinear Dynamic Process Monitoring.
IEEE Trans. Instrum. Meas., 2025

2024
Characteristic Identification of Flow Control Valves Based on Data-Model-Fusion in Actual Industrial Scenarios.
IEEE Trans. Ind. Electron., October, 2024

Sample-Evaluation-Enhanced Machine Learning Approach for Fault Diagnosis of Hybrid Systems.
IEEE Trans. Instrum. Meas., 2024

2023
Orthogonal Stationary Component Analysis for Nonstationary Process Monitoring.
IEEE Trans. Instrum. Meas., 2023

Fault Diagnosis of Bearings and Gears Based on LiteNet With Feature Aggregation.
IEEE Trans. Instrum. Meas., 2023

Autocorrelation Feature Analysis for Dynamic Process Monitoring of Thermal Power Plants.
IEEE Trans. Cybern., 2023

Fault Detection for Dynamic Processes Based on Recursive Innovational Component Statistical Analysis.
IEEE Trans Autom. Sci. Eng., 2023

A process monitoring criterion based on weighted contribution of principal components.
Proceedings of the CAA Symposium on Fault Detection, 2023

2022
Artificial Neural Correlation Analysis for Performance-Indicator-Related Nonlinear Process Monitoring.
IEEE Trans. Ind. Informatics, 2022

Gear Fault Diagnosis Based on Variational Modal Decomposition and Wide+Narrow Visual Field Neural Networks.
IEEE Trans Autom. Sci. Eng., 2022

2021
Degradation State Partition and Compound Fault Diagnosis of Rolling Bearing Based on Personalized Multilabel Learning.
IEEE Trans. Instrum. Meas., 2021

2020
Multistep Dynamic Slow Feature Analysis for Industrial Process Monitoring.
IEEE Trans. Instrum. Meas., 2020

2019
Integrating Economic Model Predictive Control and Event-Triggered Control: Application to Bi-Hormonal Artificial Pancreas System.
IEEE Access, 2019


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