Next Article in Journal
Linear and Nonlinear Performance Analysis of Hydrodynamic Journal Bearings with Different Geometries
Next Article in Special Issue
Critical Materials Determination as a Complement to the Product Recycling Desirability Model for Sustainability in Malaysia
Previous Article in Journal
Planning of High-Power Charging Stations for Electric Vehicles: A Review
Previous Article in Special Issue
Sustainability in the Circular Economy: Insights and Dynamics of Designing Circular Business Models
 
 
Article

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

1
College of Engineering and Physical Sciences, Aston University, Birmingham B4 7ET, UK
2
Harms & Wende GmbH & Co. KG, 21079 Hamburg, Germany
3
Information Technologies Institute, Center for Research and Technology Hellas, 57001 Thessaloniki, Greece
*
Authors to whom correspondence should be addressed.
Academic Editor: Andrew Yeh Ching Nee
Appl. Sci. 2022, 12(7), 3218; https://doi.org/10.3390/app12073218
Received: 23 February 2022 / Revised: 20 March 2022 / Accepted: 20 March 2022 / Published: 22 March 2022
(This article belongs to the Special Issue Manufacturing Sustainability in a Circular Economy)
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. View Full-Text
Keywords: circular economy; remanufacturing; predictive maintenance; condition monitoring; remaining useful life prediction; dynamic maintenance scheduling circular economy; remanufacturing; predictive maintenance; condition monitoring; remaining useful life prediction; dynamic maintenance scheduling
Show Figures

Figure 1

MDPI and ACS Style

Zhang, M.; Amaitik, N.; Wang, Z.; Xu, Y.; Maisuradze, A.; Peschl, M.; Tzovaras, D. Predictive Maintenance for Remanufacturing Based on Hybrid-Driven Remaining Useful Life Prediction. Appl. Sci. 2022, 12, 3218. https://doi.org/10.3390/app12073218

AMA Style

Zhang M, Amaitik N, Wang Z, Xu Y, Maisuradze A, Peschl M, Tzovaras D. Predictive Maintenance for Remanufacturing Based on Hybrid-Driven Remaining Useful Life Prediction. Applied Sciences. 2022; 12(7):3218. https://doi.org/10.3390/app12073218

Chicago/Turabian Style

Zhang, Ming, Nasser Amaitik, Zezhong Wang, Yuchun Xu, Alexander Maisuradze, Michael Peschl, and Dimitrios Tzovaras. 2022. "Predictive Maintenance for Remanufacturing Based on Hybrid-Driven Remaining Useful Life Prediction" Applied Sciences 12, no. 7: 3218. https://doi.org/10.3390/app12073218

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop