Fault diagnosis based on simulation models

2005 ◽  
pp. 626-635
Author(s):  
Frank Plaßmeier ◽  
Reimund Küke ◽  
D. Exner ◽  
K. F. Lehmann
Keyword(s):  
Fault Diagnosis ◽  
Simulation Models

The Faults Detection Methods for Embedded Equipment

2014 ◽  
Vol 602-605 ◽  
pp. 2041-2043
Author(s):  
Bin Wang
Keyword(s):  
Fault Diagnosis ◽  
Failure Modes ◽  
Simulation Models ◽  
Classification Model ◽  
Detection Methods ◽  
Control Procedure ◽  
Element Analysis ◽  
Embedded Control ◽  
Diagnosis Method ◽  
Simulation Results

Due to the objects in the embedded control procedure are difficult to obtain a variety of fault data and fault features, it’s necessary to establish simulation models in accordance with the operational mechanisms of the embedded equipment to simulate and diagnose the practical faults. This paper proposes a SVM integrated diagnostic method and further proposes the faults classification model with improved neural network. The faults diagnose performance is greatly improved by analyzing the types of the faults in different facets. For the embedded valve failure modes, the simulation results of the proposed method are compared with that of the previous mature independent element analysis method. The simulation results show that the fault diagnosis method in this paper can effectively improve the speed and accuracy of fault diagnosis for the embedded equipment.

Correlation of Pore Structure and Permeability by SEM-Image Analysis

1990 ◽  
Vol 48 (1) ◽  
pp. 428-429
Author(s):  
C. A. Callender ◽  
Wm. C. Dawson ◽  
J. J. Funk
Keyword(s):  
Image Analysis ◽  
Pore Structure ◽  
Imaging System ◽  
Pore Space ◽  
Simulation Models ◽  
Image Analyzer ◽  
Imaging Data ◽  
Sem Images ◽  
Thin Sections ◽  
Rock Types

The geometric structure of pore space in some carbonate rocks can be correlated with petrophysical measurements by quantitatively analyzing binaries generated from SEM images. Reservoirs with similar porosities can have markedly different permeabilities. Image analysis identifies which characteristics of a rock are responsible for the permeability differences. Imaging data can explain unusual fluid flow patterns which, in turn, can improve production simulation models.Analytical SchemeOur sample suite consists of 30 Middle East carbonates having porosities ranging from 21 to 28% and permeabilities from 92 to 2153 md. Engineering tests reveal the lack of a consistent (predictable) relationship between porosity and permeability (Fig. 1). Finely polished thin sections were studied petrographically to determine rock texture. The studied thin sections represent four petrographically distinct carbonate rock types ranging from compacted, poorly-sorted, dolomitized, intraclastic grainstones to well-sorted, foraminiferal,ooid, peloidal grainstones. The samples were analyzed for pore structure by a Tracor Northern 5500 IPP 5B/80 image analyzer and a 80386 microprocessor-based imaging system. Between 30 and 50 SEM-generated backscattered electron images (frames) were collected per thin section. Binaries were created from the gray level that represents the pore space. Calculated values were averaged and the data analyzed to determine which geological pore structure characteristics actually affect permeability.

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