稀疏灰色预测模型的学习和应用,以疲劳寿命预测飞机的膝关节结构
Lu Yang1, Qiuhui Xu2, Baolei Wei3
1College of Information Management, Nanjing Agricultural University, Nanjing, China; College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
ISA transactions
|May 13, 2025
概括
本研究引入了一种新的数据驱动方法,用于灰色预测模型,同时发现模型结构和估计测量噪声. 该方法准确地识别了复杂的动态,并预测了飞机结构中的疲劳裂传播.
科学领域:
- 工程 工程师 工程师 工程师
- 数据科学数据科学数据科学
- 材料科学 材料科学 材料科学
背景情况:
- 灰色预测模型对于分析时间序列数据至关重要,但在模型结构发现方面面临挑战.
- 当前的方法往往依赖于累积总和运算符,需要大量的专业知识和预定义的模型形状.
- 准确的噪声估计对于可靠的灰色模型性能至关重要.
研究的目的:
- 开发一个数据驱动的范式,用于同时学习灰色模型结构和测量噪声估计.
- 提高灰色预测模型的预测能力和可解释性.
- 将拟议的方法应用于现实世界的问题,如疲劳裂传播分析.
主要方法:
- 使用一个状态空间框架与灰色模型的整体表示.
- 设计一个候选特征库,用于显式模型方程表示,通过稀疏学习解决.
- 制定一个规范化的目标函数,将信号噪声分解和联合学习的时间分步限制结合起来.
- 进行大规模模拟来评估有限样本的性能.
主要成果:
- 在灰色模型结构学习中表现出高精度.
- 即使在显著的测量噪声下,也表现出强度和高性能.
- 成功地应用到飞机膝关节的疲劳裂传播数据.
- 准确地发现动态模式并预测剩余的使用寿命.
结论:
- 拟议的数据驱动方法在灰色预测模型开发方面取得了重大进展.
- 它有效地解决了模型结构发现和噪声估计的挑战.
- 这种方法显示出在工程诊断和预测,特别是结构健康监测中应用的巨大潜力.
相关概念视频
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01:22Fatigue Strength of Concrete
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01:23Stresses under Combined Loadings
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01:25Yield Criteria for Ductile Materials under Plane Stress
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01:28Internal Loadings in Structural Members: Problem Solving
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