Comparison of agent deployment strategies for collaborative prognosis

Dhada, Maharshi; Hernandez, Marco Perez; Salvador Palau, Adria; Parlikad, Ajith

Abstract

Collaborative prognosis is a technique that enables the industrial assets to learn from similar other assets in a fleet, and improve their data-driven prognosis models. When collaborative prognosis is implemented in a computationally distributed framework, each asset is monitored by its corresponding Digital Twin agent. Distributed collaborative prognosis is particularly beneficial for high value assets where the communication and the processing costs are negligible compared to the maintenance costs. This paper analyses the effects of Digital Twin deployment strategies on the effectiveness of predictive maintenance activities relying on distributed collaborative prognosis. Distributed and heterarchical multi-agent system architectures are analysed for large fleets of assets, with varying failure rates and noise levels in the failure data. The results show that no single architecture or deployment strategy can be deemed best across all failure rates and noise levels. The conclusion derived in this paper provides guidance to the asset owners to choose the most suitable combination for a given application.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2021 IEEE International Conference on Prognostics and Health Management, ICPHM 2021
• Start Date: Jun 7, 2021
• End Date: Jun 9, 2021
• Acceptance Date: Mar 30, 2021
• Online Publication Date: Jul 20, 2021
• Publication Date: Jul 20, 2021
• Deposit Date: Feb 1, 2022
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Book Title: 2021 IEEE International Conference on Prognostics and Health Management (ICPHM)
• ISBN: 9781665429962
• DOI: https://doi.org/10.1109/ICPHM51084.2021.9486628
• Public URL: https://uwe-repository.worktribe.com/output/8543518
• Publisher URL: https://ieeexplore.ieee.org/document/9486628