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.
Details
Title
- Data driven framework for prognostics
Contributors
- Varadarajan, Gayathri (Author)
- Liu, Huan (Thesis advisor)
- Ye, Jieping (Committee member)
- Davalcu, Hasan (Committee member)
- Arizona State University (Publisher)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2010
Subjects
Resource Type
Collections this item is in
Note
- thesisPartial requirement for: M.S., Arizona State University, 2010
- bibliographyIncludes bibliographical references (p. 36-37)
- Field of study: Computer science
Citation and reuse
Statement of Responsibility
by Gayathri Varadarajan