高精度控制时刻陀螺仪故障诊断通过联合注意力机制
Rongqiang Guan1,2, Qiongying Lv3, Bing Jia4
1Changchun University of Science and Technology, Changchun, 130000, Jilin, China. R.Q.Guan@mails.cust.edu.cn.
Scientific reports
|April 22, 2025
概括
人工卫星控制瞬间陀螺仪 (CMG) 的及时故障诊断至关重要. 使用深度学习的联合注意力机制 (JAM) 的新方法提高了从振动信号中检测CMG故障的准确性和稳定性.
科学领域:
- 人工智能的人工智能
- 航空航天工程 航空航天工程
- 机械工程 机械工程
背景情况:
- 控制瞬间陀螺仪 (CMG) 是关键的卫星系统;它们的故障会造成重大经济和运营损失.
- 传统的故障诊断方法由于局部特征陷和提取困难而与CMG振动信号作斗争,限制了准确性和稳定性.
- 准确和及时评估CMG的健康状况对于任务的成功和安全至关重要.
研究的目的:
- 为控制瞬间陀螺仪 (CMG) 开发一个高精度故障诊断模型.
- 克服分析CMG振动信号的传统方法的局限性,特别是解决时间变化的特征和旋转速度的影响.
- 为了提高CMG故障诊断的准确性和稳定性.
主要方法:
- 一种新的故障诊断方法,将联合注意力机制 (JAM) 与一维扩展卷积网络和剩余连接相结合.
- 该JAM被用来有效地从振动信号中学习特征信息,专注于与故障相关的特征.
- 来自三个不同的旋转速度的数据集被混合在一起,以训练和验证模型,测试其对速度变化的弹性.
主要成果:
- 拟议的方法通过有效地解决振动信号的时间变化的性质,证明了卓越的特征学习能力.
- 联合注意力机制 (JAM) 减轻了不同旋转速度对诊断准确性的影响.
- 在自行收集的数据集上进行实验验证,其准确度为98.14%,在CMG故障诊断中具有很高的稳定性.
结论:
- 将JAM与深度学习架构集成为CMG故障诊断提供了显著的进步.
- 拟议的方法有效地从复杂的振动信号中提取关键特征,从而提高诊断性能.
- 这种方法为人工卫星中CMG的实时健康监测提供了强大而准确的解决方案.
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