Dynamic sensor fault detection approach using data-driven techniques

Sensor fault detection is an important phase for process surveillance. Indeed, successful execution of process tasks depends on the state of the available data. In industrial applications, systems have an uncertain behavior, so methods based on interval principles are useful in this context, such as...

Full description

Saved in:
Bibliographic Details
Published inNeural computing & applications Vol. 36; no. 23; pp. 14291 - 14307
Main Authors Hamrouni, Imen, Abdellafou, Khaoula Ben, Aborokbah, Majed, Taouali, Okba
Format Journal Article
LanguageEnglish
Published London Springer London 01.08.2024
Springer Nature B.V
Subjects
Air monitoring
Air quality
Artificial Intelligence
Comparative studies
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Datasets
Fault detection
Image Processing and Computer Vision
Industrial applications
Methods
Original Article
Principal components analysis
Probability and Statistics in Computer Science
Process controls
Sensors
Sliding
Process monitoring
Q
MW-IKPCA
Machine learning
Sliding window
Tennessee Eastman process
Interval-fault detection
Anomaly detection
Air quality monitoring network
Online AccessGet full text

Cover

More Information
Summary:Sensor fault detection is an important phase for process surveillance. Indeed, successful execution of process tasks depends on the state of the available data. In industrial applications, systems have an uncertain behavior, so methods based on interval principles are useful in this context, such as interval kernel principal component analysis (IKPCA) and Interval-Kernel-Partial-Least Square (IKPLS). These techniques do not always achieve efficiency since they are based on an invariant time. To overcome this restriction, we used a sliding windows principle to develop these methods in dynamic uncertain systems. In addition, a small size of the sliding window can improve the computation time and adapt to the rapidly changing process dynamics. It is for this reason that we propose a second method; the reduced rank moving window IKPCA method (MW-RRIKPCA), In order to improve the performance of the suggested methods based on sliding windows, compared to classical ones, we carried out a comparative study using Tennessee Eastman process (TEP) data and the air quality monitoring network (AIRLOR).
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-024-09847-z