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

Applied Sciences

Nasser Amaitik¹  Zezhong Wang¹  Yuchun Xu¹  Ming Zhang¹  Alexander Maisuradze²  Michael Peschl²  Dimitrios Tzovaras³ 

[1] College of Engineering and Physical Sciences, Aston University, Birmingham B4 7ET, UK;Harms & Wende GmbH & Co. KG, 21079 Hamburg, Germany;Information Technologies Institute, Center for Research and Technology Hellas, 57001 Thessaloniki, Greece;

关键词: circular economy;    remanufacturing;    predictive maintenance;    condition monitoring;    remaining useful life prediction;    dynamic maintenance scheduling;   

DOI  :  10.3390/app12073218

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown