| DOI | Resolve DOI: https://doi.org/10.1109/IJCNN.2017.7966245 |
|---|
| Author | Search for: Cheung, Catherine1; Search for: Valdés, Julio J.2; Search for: Rubio, Alejandro Lehman1 |
|---|
| Name affiliation | - National Research Council of Canada. Aerospace
- National Research Council of Canada. Information and Communication Technologies
|
|---|
| Format | Text, Article |
|---|
| Proceedings title | 2017 International Joint Conference on Neural Networks (IJCNN) |
|---|
| Series title | Proceedings of International Joint Conference on Neural Networks |
|---|
| Conference | International Joint Conference on Neural Networks (IJCNN), 14-19 May 2017, Anchorage, AK, USA |
|---|
| ISSN | 9781509061822
1509061827
2161-4393
2161-4407 |
|---|
| Pages | 3122–3129 |
|---|
| Abstract | The accurate estimation of helicopter component loads is an important goal for ensuring safe operation as well as for life cycle management and life extension efforts. In this research, the use of computational intelligence, neural network, and machine learning techniques is explored to estimate helicopter component loads and their fatigue life, in particular the main rotor yoke load of the CH-146 Griffon helicopter. This paper describes efforts to reduce the number of dimensions of the input data using feature generation techniques in the load estimation methodology, beginning with intrinsic dimension analysis to determine the number of intrinsic dimensions in the data. The data set is then mapped using different implicit methods to a low-dimension representation of the original data, which is then used for load estimation and fatigue life analysis for comparison with the results of the original 26-dimension input data. The resulting load signal and fatigue life estimates from the low-dimension representations are in most cases equally if not more accurate than those for the original input data. These promising results show that the low-dimension representations retain the relevant data from the original input data set and perhaps discard spurious data resulting in more accurate estimates. |
|---|
| Publication date | 2017-07-03 |
|---|
| Publisher | IEEE |
|---|
| Language | English |
|---|
| Peer reviewed | Yes |
|---|
| NPARC number | 23002241 |
|---|
| Export citation | Export as RIS |
|---|
| Report a correction | Report a correction |
|---|
| Record identifier | 9d686a3c-01cc-4aff-8e02-3659099c064e |
|---|
| Record created | 2017-09-13 |
|---|
| Record modified | 2020-03-16 |
|---|
