Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/195122
Authors: 
Dulman, Mehmet Talha
Gupta, Surendra M.
Year of Publication: 
2018
Citation: 
[Journal:] Journal of Remanufacturing [ISSN:] 2210-4690 [Volume:] 8 [Year:] 2018 [Issue:] 3 [Pages:] 131-152
Abstract: 
The costs associated with inspecting wind turbines are high due to their size and complexity. One potential method by which such costs can be reduced, is through the development of robust systems that can monitor the conditions of wind turbines remotely. This study proposes embedding sensors into wind turbines to monitor the conditions of the wind turbines throughout their life cycles. The information retrieved from these sensors could be helpful in two ways: It could facilitate the provision of predictive maintenance for the turbines and enhance the performance of end-of-life (EOL) processing operations. During the maintenance phase, sensors can help to predict failures before they occur because they provide condition information about the products. During the EOL processing phase, they help to improve disassembly and inspection operations. Therefore, the use of embedded sensors in wind turbines could potentially reduce maintenance costs and increase EOL profit. This study compares regular and sensor-embedded wind turbine systems, which are modeled using discrete event simulation. A design of experiments study was carried out on the models. During the experimental stage, while conducting experiments, key variables, such as maintenance cost, disassembly cost, inspection cost, and EOL profit, were monitored. At the analysis stage, pairwise t-tests were performed to determine the statistical significance of the results. The results stage revealed that sensors can provide significant benefits to closed-loop supply chain systems when they are embedded into wind turbines.
Subjects: 
Maintenance
Predictive maintenance
Closed-loop supply chain
Sensor embedded products
Remanufacturing
Wind turbines
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
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