Abstract
This paper presents a novel approach to diagnosis of dc-dc converters with application to prognosis. The methodology is based on Symbolic Dynamics and Diagnostics. The data derived method builds a statistical baseline of the converter that is used to compare future statistical models of the converter as it degrades. Methods to determine the partitioning and number of partitions for the Symbolic Dynamics algorithm are discussed. In addition, a failure analysis is performed on a dc-dc forward converter to identify components with a high probability of failure. These components are then chosen to be monitored during accelerated testing of the dc-dc forward converter. The methodology is experimentally validated with data recorded from two dc-dc converters under accelerated life testing.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the Annual Conference of the Prognostics and Health Management Society 2011, PHM 2011 |
| Editors | Abhinav Saxena, Sankalita Saha, Jose R. Celaya |
| Publisher | Prognostics and Health Management Society |
| Pages | 469-478 |
| Number of pages | 10 |
| ISBN (Electronic) | 9781936263035 |
| State | Published - Jan 1 2014 |
| Event | 2011 Annual Conference of the Prognostics and Health Management Society, PHM 2011 - Montreal, Canada Duration: Sep 25 2011 → Sep 29 2011 |
Other
| Other | 2011 Annual Conference of the Prognostics and Health Management Society, PHM 2011 |
|---|---|
| Country/Territory | Canada |
| City | Montreal |
| Period | 9/25/11 → 9/29/11 |
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Software
- Electrical and Electronic Engineering
- Health Information Management