A comprehensive framework from real-time prognostics to maintenance decisions

Jain, Amit; Dhada, Maharshi; Hernandez, Marco Perez; Herrera, Manuel; Parlikad, Ajith

Abstract

Studying the influence of imperfect prognostics information on maintenance decisions is an underexplored area. To bridge this gap, a new comprehensive maintenance support system is proposed. First, a survival theory-based prognostics module employing the Weibull time-to-event recurrent neural network was deployed in which prognostics competence was enhanced by predicting the parameters of failure distribution. In conjunction with this, a new predictive maintenance (PdM) planning model was framed via a trade-off between corrective maintenance and time lost due to PdM. This optimises maintenance time based on operational and maintenance cost parameters from the historical data. The performance of the proposed framework is demonstrated using an experimental case study on maintenance planning for cutting tools within a manufacturing facility. Systematic sensitivity analysis is provided, and the impact of imperfect prognostics information on maintenance decisions is discussed. Results show that uncertainty about prediction declines as time goes on, and as uncertainty declines, the maintenance timing becomes closer to the remaining useful life. This is expected, as the risk of making a wrong decision decreases over time.

• Journal Article Type: Article
• Acceptance Date: Dec 22, 2020
• Online Publication Date: Mar 21, 2021
• Publication Date: 2021-06
• Deposit Date: Feb 1, 2022
• Publicly Available Date: Feb 2, 2022
• Journal: IET Collaborative Intelligent Manufacturing
• Electronic ISSN: 2516-8398
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 3
• Issue: 2
• Pages: 175-183
• DOI: https://doi.org/10.1049/cim2.12021
• Public URL: https://uwe-repository.worktribe.com/output/8543501
• Publisher URL: https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/cim2.12021