Predictive Maintenance for Remanufacturing Based on Hybrid-Driven Remaining Useful Life Prediction


Remanufacturing is an activity of the circular economy model whose purpose is to keep the high value of products and materials. As opposed to the currently employed linear economic model, remanufacturing targets the extension of products and reduces the unnecessary and wasteful use of resources. Remanufacturing, along with health status monitoring, constitutes a key element for lifetime extension and reuse of large industrial equipment. The major challenge is to determine if a machine is worth remanufacturing and when is the optimal time to perform remanufacturing. The present work proposes a new predictive maintenance framework for the remanufacturing process based on a combination of remaining useful life prediction and condition monitoring methods. A hybrid-driven approach was used to combine the advantages of the knowledge model and historical data. The proposed method has been verified on the realistic run-to-failure rolling bearing degradation dataset. The experimental results combined with visualization analysis have proven the effectiveness of the proposed method.

Divisions: College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design
College of Engineering & Physical Sciences
Additional Information: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( This research was funded by RECLAIM project “Remanufacturing and Refurbishment Large Industrial Equipment” and received funding from the European Commission Horizon 2020 research and innovation programme under grant agreement No. 869884.
Publication ISSN: 2076-3417
Last Modified: 29 Nov 2023 13:15
Date Deposited: 25 Mar 2022 15:25
Full Text Link: 10.3390/app12073218
Related URLs: https://www.mdp ... -3417/12/7/3218 (Publisher URL)
https://ti.arc. ... ata-repository/ (Related URL)
PURE Output Type: Article
Published Date: 2022-04-01
Published Online Date: 2022-03-22
Accepted Date: 2022-03-20
Authors: Zhang, Ming
Amaitik, Nasser
Wang, Zezhong
Xu, Yuchun
Maisuradze, Alexander
Peschl, Michael
Tzovaras, Dimitrios



Version: Published Version

License: Creative Commons Attribution

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