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一种基于可解释神经网络观察者的健康状况估计方法,用于HVs.

Dengji Zhou1, Yaoxin Shen1, Yadong Wu2

  • 1The Key Laboratory of Power Machinery and Engineering of Education Ministry, Shanghai Jiao Tong University, Shanghai 200240, PR China.

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概括

这项研究引入了一个可解释的神经网络,用于高超音速车辆健康估计,减少数据依赖性和提高准确性. 这些新方法提高了故障检测,并提供了可靠的在线健康状况监测.

关键词:
健康状况估计健康状况估计超音速车辆是超音速车辆.可解释的神经网络模型神经网络的观察者是神经网络的观察者.

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科学领域:

  • 航空航天工程 航空航天工程
  • 人工智能的人工智能
  • 控制系统 控制系统

背景情况:

  • 准确的健康状况估计对于高超音速车辆的安全性和故障诊断至关重要.
  • 传统的神经网络方法存在数据依赖性和缺乏解释性,阻碍了精确的估计.
  • 挑战包括确保准确性和理解复杂动态系统中的模型行为.

研究的目的:

  • 为高超音速车辆开发先进的健康状况估计方法.
  • 解决数据依赖性问题,提高估计技术中的模型解释性.
  • 提高在线健康监测的准确性和可靠性.

主要方法:

  • 开发了一个区块可解释的神经网络模型,结合了轨迹和态度方程.
  • 建议基于可解释模型的无监督和监督健康状况估计方法.
  • 引入了FC-LN-Mish结构,用于监督方法将故障残留物映射到故障参数.

主要成果:

  • 提出的方法证明了更好的适应系统机制,增强模型可解释性和减少数据依赖性.
  • 实现了高估计效率和精度,在低故障偏差场景中表现优于其他模型.
  • FC-LN-Mish结构有效降低了错过和错误检测率.

结论:

  • 可解释神经网络观察器准确估计轮和反应控制系统 (RCS) 的健康状况参数.
  • 这些方法减少了数据依赖和处理成本,在高不确定性条件下提供了卓越的性能.
  • 提供了高超音速车辆在线健康估计的有效方法.