Fault Detection Using an Artificial Neural Network and Variable Structure Based Approach

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Sanghyun_umbc_0434M_11720.pdf (2.07 MB)

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http://hdl.handle.net/11603/15723

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UMBC Theses and Dissertations

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Author/Creator ORCID

Date

2017-01-01

Type of Work

theses
Text

Department

Mechanical Engineering

Program

Engineering, Mechanical

Citation of Original Publication

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This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
Distribution Rights granted to UMBC by the author.

Subjects

Artificial Neural Network
Fault Detection
Smooth Variable Structure Filter

Abstract

The ultimate goal of fault detection and isolation is to maximize the life span of equipment and minimize the cost of maintenance. The development of intelligent diagnostic, prognostic, and health management technology has proven to be important for industrial and defense maintenance procedures in recent years. While diagnostic technology for aircraft have existed for more than 50 years, modern CPUs permit on-board intelligent and estimation-based fault detection methods. This theses discussed two strategies in particular: artificial neural networks and smooth variable structure filters. The purpose of this theses is to propose a method of health state awareness for a helicopter blade using an artificial neural network as well as develop a variable structure-based fault detection and diagnosis strategy for an electromechanical actuator.