复杂系统异常检测通过可学习的时间空间图表与退化趋势细分

Qinfeng Han1, Jinglong Chen1, Jun Wang2

  • 1State Key Laboratory for Manufacturing and Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China.

ISA transactions
|July 10, 2024
PubMed
概括

这项研究引入了系统级异常检测 (AD) 的新框架,用于液体火箭发动机 (LREs) 等设备. 该方法通过使用先前的知识和时间空间依赖来更好地识别系统异常来提高安全性.

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