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A survey of industrial model predictive control technology
This paper provides an overview of commercially available model predictive control (MPC) technology, both linear and nonlinear, based primarily on data provided by MPC vendors. A brief history ofExpand
  • 4,160
  • 159
  • PDF
Statistical process monitoring: basics and beyond
  • S. Qin
  • Computer Science
  • 1 August 2003
TLDR
This paper provides an overview and analysis of statistical process monitoring methods for fault detection, identification and reconstruction. Expand
  • 1,170
  • 79
Recursive PLS algorithms for adaptive data modeling
Partial least squares (PLS) regression is effectively used in process modeling and monitoring to deal with a large number of variables with collinearity. In this paper, several recursive partialExpand
  • 518
  • 38
Reconstruction-Based Fault Identification Using a Combined Index
TLDR
We propose a reconstruction-based fault identification approach using a combined index for multidimensional fault reconstruction and identification. Expand
  • 362
  • 33
Recursive PCA for Adaptive Process Monitoring
TLDR
Two recursive principal component analysis (PCA) algorithms for adaptive process monitoring . Expand
  • 256
  • 32
Survey on data-driven industrial process monitoring and diagnosis
  • S. Qin
  • Engineering, Computer Science
  • Annu. Rev. Control.
  • 1 December 2012
TLDR
This paper provides a state-of-the-art review of the methods and applications of data-driven fault detection and diagnosis that have been developed over the last two decades. Expand
  • 794
  • 29
An overview of subspace identification
  • S. Qin
  • Mathematics, Computer Science
  • Comput. Chem. Eng.
  • 12 September 2006
TLDR
This paper provides an overview of the state of the art of subspace identification methods for both open-loop and closed-loop systems. Expand
  • 456
  • 29
  • PDF
Total projection to latent structures for process monitoring
Partial least squares or projection to latent structures (PLS) has been used in multivariate statistical process monitoring similar to principal component analysis. Standard PLS often requires manyExpand
  • 295
  • 26
  • PDF
Subspace approach to multidimensional fault identification and reconstruction
Fault detection and process monitoring using principal-component analysis (PCA) and partial least squares were studied intensively and applied to industrial processes. The fundamental issues ofExpand
  • 358
  • 24
Multimode process monitoring with Bayesian inference‐based finite Gaussian mixture models
For complex industrial processes with multiple operating conditions, the traditional multivariate process monitoring techniques such as principal component analysis (PCA) and partial least squaresExpand
  • 365
  • 20
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