Full metadata
Title
Data driven framework for prognostics
Description
Prognostics and health management (PHM) is a method that permits the reliability of a system to be evaluated in its actual application conditions. This work involved developing a robust system to determine the advent of failure. Using the data from the PHM experiment, a model was developed to estimate the prognostic features and build a condition based system based on measured prognostics. To enable prognostics, a framework was developed to extract load parameters required for damage assessment from irregular time-load data. As a part of the methodology, a database engine was built to maintain and monitor the experimental data. This framework helps in significant reduction of the time-load data without compromising features that are essential for damage estimation. A failure precursor based approach was used for remaining life prognostics. The developed system has a throughput of 4MB/sec with 90% latency within 100msec. This work hence provides an overview on Prognostic framework survey, Prognostics Framework architecture and design approach with a robust system implementation.
Date Created
2010
Contributors
- Varadarajan, Gayathri (Author)
- Liu, Huan (Thesis advisor)
- Ye, Jieping (Committee member)
- Davalcu, Hasan (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
viii, 40 p. : ill. (some col.)
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.8595
Statement of Responsibility
by Gayathri Varadarajan
Description Source
Viewed on Oct. 10, 2012
Level of coding
full
Note
thesis
Partial requirement for: M.S., Arizona State University, 2010
bibliography
Includes bibliographical references (p. 36-37)
Field of study: Computer science
System Created
- 2011-08-12 12:58:18
System Modified
- 2021-08-30 01:57:26
- 3 years 3 months ago
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