通过引导神经网络方法预测系统退化
Hamidreza Habibollahi Najaf Abadi1, Mohammad Modarres1
1Center for Risk and Reliability, Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
这项研究引入了一种新的数据驱动框架,使用两个神经网络,从短期传感器数据准确预测工程系统寿命. 这种方法提高了可靠性,并通过高效地建模降解物理来优化维护.
科学领域:
- 工程 工程师 工程师 工程师
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
背景情况:
- 准确的寿命估计对于工程系统的安全性和可靠性至关重要.
- 像加速生命测试这样的传统方法在模拟现实世界条件方面存在局限性.
- 在实际操作条件下测量现场退化,往往需要大量的时间.
研究的目的:
- 开发一个时间效率高,数据驱动的框架来建模场退化和预测系统寿命.
- 为应对纳入降解物理和减少神经网络广泛训练数据要求的挑战.
- 提高工程系统的寿命估计的准确性和效率.
主要方法:
- 提出了一个整合物理发现神经网络和预测神经网络的框架.
- 物理学发现网络模拟了退化物理,指导预测网络进行增强寿命估计.
- 使用来自短期实际操作条件降解测试的传感器测量.
主要成果:
- 通过在海洋环境中对钢材大气腐蚀的案例研究来证明有效性.
- 与标准神经网络模型相比,平均绝对误差降低了高达76%.
- 综合框架提供了更准确,更有效的寿命预测.
结论:
- 拟议的数据驱动框架能够有效评估系统的安全性和可靠性.
- 通过准确和及时的寿命预测,优化维护活动.
- 该方法成功地将物理降解原理纳入数据驱动模型,以提高性能.
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