Artificicial (i.e. Artificial) neural networks and their applications in diagnostics of incipient faults in rotating machinery
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Authors
Carlson, David K.
Subjects
Machinery condition monitoring
Machinery vibration monitoring
Machinery diagnostics
Neural networks
Backpropagation
Machinery vibration monitoring
Machinery diagnostics
Neural networks
Backpropagation
Advisors
Shin,Young S.
Kim, Dong Soo
Date of Issue
1991-03
Date
March 1991
Publisher
Monterey, California. Naval Postgraduate School
Language
en_US
Abstract
In an effort to curtail rising operating costs, machinery condition monitoring and diagnostics are being increasingly used as part of predictive maintenance programs. Vibration analysis is currently among the most effective tools in machinery condition monitoring and diagnostics but has proven difficult to automat fully. Artificial neural networks, patterned after neurological systems, provide a heuristic, data based approach to problems and have demonstrated robust behavior when faced with unique and noisy data. Thus neural networks may provide an alternative or complement to conventional rule based expert systems in machinery diagnostics applications. Research is presented wherein a series of neural networks utilizing the highly successful backpropagation paradigm are configured to provide machinery diagnostics for comparatively uncomplicated mechanical systems. Through observation of their responses to minor architectural changes and performance upon presentation of genuine and artificially generated vibration data, an effort is made to ascertain their utility in more complicated systems.
Type
Thesis
Description
Series/Report No
Department
Mechanical Engineering
Organization
Naval Postgraduate School
Identifiers
NPS Report Number
Sponsors
Funding
Format
187 p.
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.