使用深度学习的自动化结构螺栓微松动监控方法
Min Qin1, Zhenbo Xie1,2, Jing Xie1
1Qingdao Campus of Naval Aviation University, Qingdao 266041, China.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
这项研究引入了一种深度学习方法,用于检测飞机发动机中螺栓的早期松动. 该方法使用批量规范堆叠自动编码器 (BNSAE) 来有效和准确地监控关键组件.
科学领域:
- 航空航天工程 航空航天工程
- 机械工程 机械工程
- 人工智能的人工智能
背景情况:
- 在飞机发动机中检测螺栓松动是具有挑战性的,因为复杂的模型和信号特征.
- 早期识别微松和从非静止信号中提取特征是重要的障碍.
研究的目的:
- 通过深度学习提出一种自动化结构螺栓微松动监测方法.
- 为了解决飞机发动机当前螺栓松动检测技术的局限性.
主要方法:
- 开发了一种批量规范化堆叠自动编码器 (BNSAEs) 模型,以实现快速融合和有效性.
- 利用信号预处理的特征化功能来简化数据和保存特征.
- 将该方法应用于飞机发动机组件中的螺栓和边缘连接结构.
主要成果:
- 国民国家安全局的模型显示了快速的趋同和有效的表现.
- 信号预处理简化了数据结构,同时保持了功能完整性,减少了训练时间.
- 该方法在关键飞机发动机组件上成功验证.
结论:
- 拟议的深度学习方法为自动化结构螺栓微软度监测提供了有效的解决方案.
- BNSAE的模型和信号预处理提高了早期检测螺栓松动的效率和准确性.
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