4 edition of Application of DPCA to oil data pH model building and comparison of optimal CBM policies found in the catalog.
Application of DPCA to oil data pH model building and comparison of optimal CBM policies
Thesis (M.A.Sc.) -- University of Toronto, 2003.
|Series||Canadian theses = -- Th`eses canadiennes|
|The Physical Object|
|Pagination||1 microfiche : negative.|
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These covariance matrices are used when applying DPCA to reduce the dimensionality of oil data. DPCA is an extension of the original PCA method applied to the matrix composed of the time-shifted data vectors (see e.g.
Ku et al. () for an application of DPCA Cited by: In this paper, we apply DPCA to a set of real oil data and use the principal components as covariates in condition-based maintenance (CBM) modeling.
The CBM model (Model 1) is then compared with the CBM model which uses raw oil data as the covariates (Model 2).Cited by: An application of DPCA to oil data for CBM modeling Article in European Journal of Operational Research (1) February with 22 Reads How we measure 'reads'.
The second method is based on vector autoregressive (VAR) modeling of CM data, DPCA, and building a proportional hazards (PH) decision model using the retained principal components as covariates. These methodologies are illustrated by an example using real oil data histories obtained from spectrometric analysis of heavy-hauler truck transmission oil samples taken at regular Cited by: 1.
PCA is applied to a simulated CBM data set and two real data sets obtained from industry: oil analysis data and vibration data. Reasonable results are obtained. This paper proposes the application of a principal components proportional hazards regression model in condition-based maintenance (CBM) by: Brillinger [22, 23]; other related applications include the construction and analysis of economic indicators  and volatility modeling .
A key point in the implementation of the DPCA method is the selection of the number of lags to be used, i.e. the number of shifted versions for each variable to include in the DPCA by: 1) model, neural network model, support vector machine model, etc.
Oil field development system is a complex multi variables non-linear dynamical systems, different predicting model has different characteristics like pre- dicting accuracy.
Neural network model and support vec- tor machine model are two effective methods to solve. The CBM model (Model 1) is then compared with the CBM model which uses raw oil data as the covariates (Model 2). Using the PDCA cycle in the real world.
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ADRM Software's Upstream Oil & Gas data model set consists of Enterprise. DPCA covariates, derived from DPCA of the oil data, represent most of the variability in the original data with reduced dimension and little cross-correlation.
This makes DPCA covariates ideal for. A Control-Limit Policy And Software For Condition-Based Maintenance Optimization.
real oil and vibration data is reported. applied to find the optimal condition based maintenance model [ The application of this method to chemical event detection is based in the idea that the appearance of a new component in con- tact to the sensor array will produce a new source of variance.
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No freedom from any patent is granted or to be inferred. Cautions for handling and storage of KAYARD, KAYAMER, and KAYACURE DPCA KAYARAD DPCA KAYARAD DPCA KAYARAD DPCA KAYARAD DN Mixture of multifunctional KAYARAD.Using these data, the model is then used to predict the production from all major oil producing countries, regions and continents up to the year The limited regional and global potential to compensate this decline with unconventional oil and oil-equivalents is also presented.
Keywords: After the oil peak, regional oil production and.