Recurrent Neural Networks for real-time distributed collaborative prognostics

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. 1 1 Sponsors: Adria Salvador work was sponsored by a Doctoral Scholarship provided by “Fundacio la Caixa”. This research was also supported by Sus-tainOwner (Sustainable Design and Management of Industrial Assets through Total Value and Cost of Ownership) a project sponsored by the EU Framework Programme Horizon 2020, MSCA- RISE-2014: Marie Skodowska-Curie Research and Innovation Staff Exchange (Rise) (grant agreement number 645733 Sustain-owner H2020-MSCA-RISE-2014). Copyright: 978-1-5090-0382-2/16/$31.00 ©2018 IEEE