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New stochastic modeling approach for...

Mohon, Sara.

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New stochastic modeling approach for diagnosing faults in quantized systems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	New stochastic modeling approach for diagnosing faults in quantized systems./
Author:	Mohon, Sara.
Description:	125 p.
Notes:	Source: Dissertation Abstracts International, Volume: 77-01(E), Section: B.
Contained By:	Dissertation Abstracts International77-01B(E).
Subject:	Automotive engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3722423
ISBN:	9781339045344

New stochastic modeling approach for diagnosing faults in quantized systems.
Mohon, Sara.

New stochastic modeling approach for diagnosing faults in quantized systems. - 125 p.

Source: Dissertation Abstracts International, Volume: 77-01(E), Section: B.

Thesis (Ph.D.)--Clemson University, 2015.

Faults are defined as malfunction of a particular component and can cause significant damage to equipment and human lives if not identified early. This is especially true for systems such as automobiles where the main purpose is to transport human beings. Fault detection and isolation is an extremely important tool for systems engineering that aims to minimize this risk of damage. This dissertation first presents an overview of existing methods for determining state transition probabilities for a quantized model-based diagnostic problem. The existing approaches are Monte Carlo simulation, a cell-to-cell mapping method named Generalized Cell Mapping (GCM), and the Hyperbox-Mapping method. Limitations in these approaches are highlighted and a new method is presented to address these limitations.

ISBN: 9781339045344Subjects--Topical Terms:

2181195
Automotive engineering.

New stochastic modeling approach for diagnosing faults in quantized systems.
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020 $a 9781339045344
035 $a (MiAaPQ)AAI3722423
035 $a AAI3722423
040 $a MiAaPQ $c MiAaPQ
100 1 $a Mohon, Sara. $3 3186522
245 1 0 $a New stochastic modeling approach for diagnosing faults in quantized systems.
300 $a 125 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-01(E), Section: B.
500 $a Adviser: Pierluigi Pisu.
502 $a Thesis (Ph.D.)--Clemson University, 2015.
520 $a Faults are defined as malfunction of a particular component and can cause significant damage to equipment and human lives if not identified early. This is especially true for systems such as automobiles where the main purpose is to transport human beings. Fault detection and isolation is an extremely important tool for systems engineering that aims to minimize this risk of damage. This dissertation first presents an overview of existing methods for determining state transition probabilities for a quantized model-based diagnostic problem. The existing approaches are Monte Carlo simulation, a cell-to-cell mapping method named Generalized Cell Mapping (GCM), and the Hyperbox-Mapping method. Limitations in these approaches are highlighted and a new method is presented to address these limitations.
520 $a The main objective of this dissertation is the development of a new stochastic method to calculate state transition probabilities in a quantized system for the purpose of diagnostics. The new stochastic method exploits the Divergence Theorem and system vector field to calculate state transition probabilities. Major advantages of this new method include decreased computational burden with increasing dimensionality and no repetitive sampling and computations needed to infer state transitions. Results show that the new method can be used for fault diagnosis.
590 $a School code: 0050.
650 4 $a Automotive engineering. $3 2181195
690 $a 0540
710 2 $a Clemson University. $b Automotive Engineering. $3 1684493
773 0 $t Dissertation Abstracts International $g 77-01B(E).
790 $a 0050
791 $a Ph.D.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3722423