Monitoring multivariate process using improved Independent component analysis-generalized likelihood ratio strategy
- 12 August 2020
- journal article
- Published by Elsevier BV in IFAC-PapersOnLine
- Vol. 53 (1), 392-397
- https://doi.org/10.1016/j.ifacol.2020.06.066
Abstract
No abstract availableThis publication has 16 references indexed in Scilit:
- Change point and fault detection using Kantorovich DistanceJournal of Process Control, 2019
- Anomaly detection using multi-scale dynamic principal component analysis for Tenneesse Eastman ProcessPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2019
- A Bibliometric Review and Analysis of Data-Driven Fault Detection and Diagnosis Methods for Process SystemsIndustrial & Engineering Chemistry Research, 2018
- Kernel PLS-based GLRT method for fault detection of chemical processesJournal of Loss Prevention in the Process Industries, 2016
- A new fault detection method for non-Gaussian process based on robust independent component analysisProcess Safety and Environmental Protection, 2014
- A process monitoring method based on noisy independent component analysisNeurocomputing, 2014
- Statistical fault detection using PCA-based GLR hypothesis testingJournal of Loss Prevention in the Process Industries, 2012
- Non-Bayesian Detection and Detectability of Anomalies From a Few Noisy Tomographic ProjectionsIEEE Transactions on Signal Processing, 2007
- Independent component analysis: algorithms and applicationsNeural Networks, 2000
- The quadruple-tank process: a multivariable laboratory process with an adjustable zeroIEEE Transactions on Control Systems Technology, 2000