太空飞轮旋转系统的智能早期故障诊断
Hui Liao1, Pengfei Xie2,3, Sier Deng1,4
1School of Mechatronics Engineering, Northwestern Polytechnical University, Xi'an 710071, China.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
这项研究引入了一种新的智能方法,用于在太空飞轮旋转系统中进行早期故障诊断,解决数据不足和缺失故障类型等挑战. 拟议的等级分支和相似性聚类多通道卷积神经网络 (HB-SC-MCCNN) 提高了诊断准确度.
科学领域:
- 航空航天工程 航空航天工程
- 机器学习 机器学习
- 错误诊断 错误诊断 错误诊断 是一个
背景情况:
- 太空飞轮旋转系统的早期故障诊断面临各种挑战,包括各种故障类型,不足的标记数据和缺失的故障类别.
- 现有的深度学习模型很难有效地解决复杂的航空航天系统中的这些综合问题.
- 准确和及时的故障诊断对于太空飞轮旋转系统的可靠性和寿命至关重要.
研究的目的:
- 提出一种智能早期故障诊断方法,克服标记数据不足和缺失故障类型的局限性.
- 开发一种能够对太空飞轮旋转系统进行层次诊断的新型深度学习架构.
- 为了提高故障诊断方法的竞争力,在有限和不完整的培训数据的情况下.
主要方法:
- 开发一个具有层次分支和相似性集群的多通道卷积神经网络 (HB-SC-MCCNN).
- 将相似性聚类 (SC) 方法与参数共享双MCCNN作为核心架构的集成.
- 实施具有额外损失的等级分支模型,以简化多级分类到多步二进制分类.
- 利用模型的自学能力,重新标记未标记的数据,并将缺失的故障类型纳入网络重新训练.
主要成果:
- 拟议的HB-SC-MCCNN方法成功地在已建立的太空飞轮旋转系统的早期故障数据集上实现了分层诊断.
- 实验性比较表明,HB-SC-MCCNN的性能优于几个先进的深度学习模型.
- 该方法在处理同时缺乏标记数据和缺失故障类型的场景方面表现出显著的竞争力.
结论:
- HB-SC-MCCNN为太空飞轮旋转系统的早期故障诊断提供了有效的智能解决方案,特别是在数据稀缺的情况下.
- 层次分支和相似性聚类方法提高了诊断的稳定性和准确性.
- 这种方法为通过先进的机器学习技术提高关键航空航天组件的可靠性提供了一个有希望的方向.
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