Statistical process monitoring: basics and beyond

By S. Qin. 2003

1,356 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3824681dd9b4784ac81fe88a157fd8a33c445f81

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In Analytical Chemistry

In Control and Systems Engineering

In Statistics and Probability

Citations in the corpus

Identification of Geographical Origin of Olive Oil Using Visible and Near-Infrared Spectroscopy Technique Combined with Chemometrics (2009, methodology): “In particular, PLS has been shown powerful to build a regression model for collinear and high-dimensional data (Qin 2003).”
Data Mining and Analytics in the Process Industry: The Role of Machine Learning (2017, methodology): “multi-model/mixture PCA, adaptive/recursive PCA, dynamic PCA, multiway PCA, and so on [32]–[41].”
On reducing false alarms in multivariate statistical process control (2010, background): “…component analysis (PCA) (Wold and Geladi, 1987), partial least squares (PLS) (Geladi and Kowalski, 1986) and their non-linear variants (Choi et al., 2005; Zhao et al., 2006), to extract relevant process information, and to attain an enhanced understanding of process behaviour (Qin, 2003).”
A Review on Basic Data-Driven Approaches for Industrial Process Monitoring (2014, background): “A combined index [26], [38] which simultaneously uses both test statistics is proposed, i.”
A Review of Fault Detection and Diagnosis for the Traction System in High-Speed Trains (2020, methodology): “Typically, principal component analysis (PCA), partial least squares (PLS), and canonical correlation analysis (CCA) are the fundamental methods in feature extraction [42], [51], [57],”
Sequential Fault Diagnosis Based on LSTM Neural Network (2018, methodology): “Qin [10] were mainly focused on the reconstruction- and contribution-based FE methods.”
Quality-Related Process Monitoring Based on Total Kernel PLS Model and Its Industrial Application (2013, methodology): “By building a PLS model on process variables and quality variables, the process data can be projected onto two low-dimension subspaces [1, 8].”
Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges (2016, background): “ervice ( Li, Cheng, Qiu, & Wu, 2009 ), to detect transient faults Jha, Li, & Seshia, 2009 ), to identify the fault propagation pathays, and diagnose the root cause variables ( Yu & Rashid, 2013 ). n Zhang and Dong (2014) , the authors proposed a multi-time-slice BN with a mixture of the Gaussian out”
Survey on data-driven industrial process monitoring and diagnosis (2012, background): “To accomplish this Valle-Cervantes et al. (2001) and Qin (2003) propose to extract the fault directions from faulty data in the residual subspace, where the normal variations are minimized.”
Performance-Driven Distributed PCA Process Monitoring Based on Fault-Relevant Variable Selection and Bayesian Inference (2016, background): “fundamental technique because of its efficiency in dimension reduction and latent feature extraction [1], [3], [9].”
Reconstruction-based contribution for process monitoring (2009, methodology): “Combined index φ The combined index by Yue and Qin [2001] combines the SPE and T 2 indices into one single index as follows”