Geometric properties of partial least squares for process monitoring
From MaRDI portal
Publication:985292
DOI10.1016/j.automatica.2009.10.030zbMath1233.62208OpenAlexW2039839604MaRDI QIDQ985292
S. Joe Qin, Gang Li, Dong Hua Zhou
Publication date: 20 July 2010
Published in: Automatica (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.automatica.2009.10.030
Lua error in Module:PublicationMSCList at line 37: attempt to index local 'msc_result' (a nil value).
Related Items (15)
Quality-related fault detection using linear and nonlinear principal component regression ⋮ Direct projection to latent variable space for fault detection ⋮ A new data-driven process monitoring scheme for key performance indictors with application to hot strip mill process ⋮ Improved key performance indicator-partial least squares method for nonlinear process fault detection based on just-in-time learning ⋮ Fault detection for industrial processes ⋮ Process monitoring using a generalized probabilistic linear latent variable model ⋮ Assessment of \(T^2\)- and \(Q\)-statistics for detecting additive and multiplicative faults in multivariate statistical process monitoring ⋮ A novel dynamic non-Gaussian approach for quality-related fault diagnosis with application to the hot strip mill process ⋮ Fuzzy fault isolation using gradient information and quality criteria from system identification models ⋮ Multimode process monitoring method based on multiblock projection nonnegative matrix factorization ⋮ Quality-relevant fault detection and diagnosis for hot strip mill process with multi-specification and multi-batch measurements ⋮ Data-driven design of fault detection and isolation method for distributed homogeneous systems ⋮ A practical propagation path identification scheme for quality-related faults based on nonlinear dynamic latent variable model and partitioned Bayesian network ⋮ A key performance indicator-based fault detection scheme for marine diesel turbocharging systems ⋮ Hybrid variable monitoring: an unsupervised process monitoring framework with binary and continuous variables
Cites Work
- Reconstruction-based contribution for process monitoring
- A weighted view on the partial least-squares algorithm
- Detecting and isolating multiple plant-wide oscillations via spectral independent component analysis
- On the structure of partial least squares regression
- Fault detection and diagnosis in industrial systems
This page was built for publication: Geometric properties of partial least squares for process monitoring