Monitoring flames in an industrial boiler using multivariate image analysis

Yu, Hao; MacGregor, John F.

doi:10.1002/aic.10164

Cited by 68 publications

(40 citation statements)

References 5 publications

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“…Extraction of image features with principle component analysis (PCA) followed by PLS or artificial neural networks (ANN) has been used to obtain global quantitative information [10][11][12]. To apply PLS directly to a hyperspectral image, individual Y-block reference values are needed at every pixel of all calibration images.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral NIR image regression part II: dataset preprocessing diagnostics

Burger

Geladi

2006

Journal of Chemometrics

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When known reference values such as concentrations are available, the spectra from near infrared (NIR) hyperspectral images can be used for building regression models. The sets of spectra must be corrected for errors, transformed to reflectance or absorbance values, and trimmed of bad pixel outliers in order to build robust models and minimize prediction errors. Calibration models can be computed from small (<100) sets of spectra, where each spectrum summarizes an individual image or spatial region of interest (ROI), and used to predict large (>20 000) test sets of spectra. When the distributions of these large populations of predicted values are viewed as histograms they provide mean sample concentrations (peak centers) as well as uniformity (peak widths) and purity (peak shape) information. The same predicted values can also be viewed as concentration maps or images adding spatial information to the uniformity or purity presentations. Estimates of large population statistics enable a new metric for determining the optimal number of model components, based on a combination of global bias and pooled standard deviation values computed from multiple test images or ROIs. Two example datasets are presented: an artificial mixture design of three chemicals with distinct NIR spectra and samples of different cheeses. In some cases it was found that baseline correction by taking first derivatives gave more useful prediction results by reducing optical problems. Other data pretreatments resulted in negligible changes in prediction errors, overshadowed by the variance associated with sample preparation or presentation and other physical phenomena.

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Section: Introductionmentioning

confidence: 99%

Hyperspectral NIR image regression part II: dataset preprocessing diagnostics

Burger

Geladi

2006

Journal of Chemometrics

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“…Applications involve inferential modelling, but also process monitoring and control [8,9,10,[77][78][79]. The methodology can be used to monitor solids and other heterogeneous materials (lumber, steel sheets, pulp and paper products, polymer films, multiphase streams, etc.…”

Section: Image Analysismentioning

confidence: 99%

Application of latent variable methods to process control and multivariate statistical process control in industry

Kourti

2005

Adaptive Control & Signal

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Multivariate monitoring and control schemes based on latent variable methods have been receiving increasing attention by industrial practitioners in the last 15 years. Several companies have enthusiastically adopted the methods and have reported many success stories. Applications have been reported where multivariate statistical process control, fault detection and diagnosis is achieved by utilizing the latent variable space, for continuous and batch processes, as well as, for process transitions as for example start ups and re-starts. This paper gives an overview of the latest developments in multivariate statistical process control (MSPC) and its application for fault detection and isolation (FDI) in industrial processes. It provides a critical review of the methodology and describes how it is transferred to the industrial environment. Recent applications of latent variable methods to process control as well as to image analysis for monitoring and feedback control are discussed. Finally it is emphasized that the multivariate nature of the data should be preserved when data compression and data preprocessing is applied. It is shown that univariate data compression and reconstruction may hinder the validity of multivariate analysis by introducing spurious correlations.

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“…This is quite common for solid matrices or multiphase products and several solutions are currently being implemented with success in industry for conducting such tasks in an autonomous way [1][2][3][4][5]. The type of methodology adopted depends on the relevant characteristics of the product depicted in the images.…”

Section: Introductionmentioning

confidence: 99%