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https://dspace.iiti.ac.in/handle/123456789/6779
| Title: | Recurrent Neural Networks for real-time distributed collaborative prognostics |
| Authors: | Bakliwal, Kshitij Dhada, Maharshi Harshadbhai |
| Keywords: | Systems engineering;Real time;Similarity analysis;Time to events;Time to failure;Weibull;Recurrent neural networks |
| Issue Date: | 2018 |
| Publisher: | Institute of Electrical and Electronics Engineers Inc. |
| Citation: | Palau, A. S., Bakliwal, K., Dhada, M. H., Pearce, T., & Parlikad, A. K. (2018). Recurrent neural networks for real-time distributed collaborative prognostics. Paper presented at the 2018 IEEE International Conference on Prognostics and Health Management, ICPHM 2018, doi:10.1109/ICPHM.2018.8448622 |
| Abstract: | We present the first steps towards real-time distributed collaborative prognostics enabled by an implementation of the Weibull Time To Event - Recurrent Neural Network (WTTE-RNN) algorithm. In our system, assets determine their time to failure (TTF) in real-time according to an asset-specific model that is obtained in collaboration with other similar assets in the asset fleet. The presented approach builds on the emergent field of similarity analysis in asset management, and extends it to distributed collaborative prognostics. We show how through collaboration between assets and distributed prognostics, competitive time to failure estimates can be obtained. © 2018 IEEE. |
| URI: | https://doi.org/10.1109/ICPHM.2018.8448622 https://dspace.iiti.ac.in/handle/123456789/6779 |
| ISBN: | 9781538611647 |
| Type of Material: | Conference Paper |
| Appears in Collections: | Department of Mechanical Engineering |
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