Abstract
Optimization of operations and maintenance activities in factories was estimated to have a global economic potential of 1.2 to 3.7 trillion USD by recent studies. Digital twins offer a framework to achieve such optimization by studying potential improvements in the virtual space before applying them to the real world. We studied the use of a digital twin based on a general model of system failure behaviour for maintenance optimization by combining existing methodologies into a general framework. Applying it to a real-world power converter use case, we identified either reactive or preventive maintenance to be more cost-effective depending on the operating conditions. This allowed to predict optimal maintenance for existing and future systems.
| Item Type: | Journal article |
|---|---|
| Faculties: | Mathematics, Computer Science and Statistics > Computer Science |
| Subjects: | 000 Computer science, information and general works > 004 Data processing computer science |
| Language: | English |
| Item ID: | 82245 |
| Date Deposited: | 15. Dec 2021, 15:01 |
| Last Modified: | 15. Dec 2021, 15:01 |
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