Survey on data-driven industrial process monitoring and diagnosis
By S. Qin. Annual Reviews in Control, 2012
1,368 citations recorded by the source corpus.
Subfields: Analytical Chemistry, Control and Systems Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/653e2c5fa6c325207df38c300f60dcd3c247f215
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In Analytical Chemistry
In Control and Systems Engineering
In Mechanical Engineering
Citations in the corpus
- A Review of Fault Detection and Diagnosis for the Traction System in High-Speed Trains (2020, methodology): “The commonly used test statistics to directly monitor the operation of high-speed trains are the Hotelling’s T 2 statistic and the squared prediction error (SPE) statistic [57] [113].”
- Data augmentation for improving deep learning in image classification problem (2018, background): “Atypical cases such as data collected during detection of variety of industrial plant and other faults [27], [28] detection and localization of leakages in drinking water distribution networks [29], [30], [31] diagnostics of the induction motor [32], [33] or other machines [34], and all kind of medi”
- An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series (2021, background): “learning based techniques have received significant attention for MVTS anomaly detection owing to (a) their ability to scale to high dimensions and model complex patterns in various domains, compared to straightforward statistical approaches such as out of limits approaches [5], [29], [30], (b) fast”
- Sequential Fault Diagnosis Based on LSTM Neural Network (2018, methodology): “and dynamic linear discriminant analysis (DLDA) [3], [16] were adopted.”
- Canonical correlation analysis-based fault detection methods with application to alumina evaporation process (2016, methodology): “Common data-driven methods are based on multivariate analysis (MVA) techniques, such as principal component analysis (PCA) and partial least square (PLS), which have attracted more and more attention from academia and industry (Qin, 2012).”