Recurrent Neural Networks for real-time distributed collaborative prognostics
Accepted version
Peer-reviewed
Repository URI
Repository DOI
Change log
Authors
Parlikad, AKN
Salvador Palau, Adria
Bakliwal, Kshitij
Dhada, Maharshi Harshadbhai
Pearce, Tim
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.
Description
Keywords
46 Information and Computing Sciences, 35 Commerce, Management, Tourism and Services, 3507 Strategy, Management and Organisational Behaviour
Journal Title
2018 IEEE International Conference on Prognostics and Health Management (ICPHM)
Conference Name
2018 IEEE International Conference on Prognostics and Health Management
Journal ISSN
Volume Title
Publisher
IEEE
Publisher DOI
Sponsorship
European Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (645733)
European Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (645733)
Engineering and Physical Sciences Research Council (EP/R004935/1)
European Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (645733)
Engineering and Physical Sciences Research Council (EP/R004935/1)