Abstract
A prognostic framework is proposed in order to estimate the remaining useful life of composite materials under fatigue loading based on acoustic emission data and a sophisticated Non Homogenous Hidden Semi Markov Model. Bayesian neural networks are also utilized as an alternative machine learning technique for the non-linear regression task. A comparison between the two algorithms operation, input, output and performance highlights their ability to tackle the prognostic task.
Original language | English |
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Title of host publication | 8th European Workshop on Structural Health Monitoring |
Subtitle of host publication | Bilbao, Spain |
Publisher | NDT.net |
Pages | 859-868 |
Number of pages | 10 |
Volume | 2 |
ISBN (Print) | 9781510827936 |
Publication status | Published - 2016 |
Event | 8th European Workshop on Structural Health Monitoring - Bilbao, Spain Duration: 5 Jul 2016 → 8 Jul 2016 Conference number: 8 http://www.ndt.net/events/EWSHM2016/app/content/ |
Conference
Conference | 8th European Workshop on Structural Health Monitoring |
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Abbreviated title | EWSHM 2016 |
Country/Territory | Spain |
City | Bilbao |
Period | 5/07/16 → 8/07/16 |
Internet address |
Keywords
- SHM
- remaining useful life
- machine learning techniques
- composite materials
- acoustic emission