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Recurrent Neural Networks for real-time distributed collaborative prognostics

Accepted version
Peer-reviewed

Type

Conference Object

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
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)