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Agent-Based Automated Algorithm Generator
| 题名： | Agent-Based Automated Algorithm Generator |
| 作者： | Zhang, Guangfan;Xu, Roger;Liu, Xiong;Lyell, M;Zhang, Xiaodong;Bechtel, James; |
| 关键词： | agent-based；generator；algorithm；diagnostic；mate；auto；statistics；capabilities；component；variability |
| 摘要： | The variability of vehicles poses a great challenge on the diagnostics and prognostics for the whole fleet with a vast number of Army ground vehicle platforms. A general diagnostics/prognostics model does not exist and it is difficult to select the best algorithm from a large amount of candidate algorithms for each specific component/subsystem/system application. Therefore, it is necessary to develop a unified framework to evaluate and select the best algorithms, and further maintain the on-vehicle algorithms by updating algorithm parameters and integrating new fleet-wide vehicle data statistics and trends. To address this problem, we propose an agent-based automated algorithm generator for fleet-wide diagnostics/prognostics, which can automatically generate the most suitable algorithm(s) for each vehicle or component in the fleet from a library of light-weight diagnostic/prognostic algorithms. When sufficient fleet-wide statistics and trending information are available, the automated algorithm generator server will automatically determine whether it is necessary to update the current vehicle algorithm configuration or select a better algorithm for on-vehicle diagnostics/prognostics. To prove the concept, we used battery diagnostics as an example to demonstrate the algorithm selection & generation process, and updating capabilities in a networked agent environment. |
| 总页数： | 12 Pages(s) |
| 报告类型： | 科技报告 |
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