基于人工神经网络和高斯过程回归的随机载荷下疲劳失效实时的预测和不确定性量化的数据驱动方法
Data-driven method for real-time prediction and uncertainty quantification of fatigue failure under stochastic loading using artificial neural networks and Gaussian process regression
摘要：
各种工程系统，如海军和航空器，海上结构，和机动系统的机械部件，暴露于疲劳失效，由于随机载荷。早期失效预测方法在工程、军事和民用领域都有重要应用。除了预测故障前时间（TtF）外，不确定性量化（UQ）对于实时的【形】决策也是非常重要的。通常，时域或频域方法用于疲劳预测，例如雨流计数和Miner规则或Dirlik方法。然而，这些方法遭受过于简单的建模和不准确的故障预测下随机载荷。在过去的几年中，提出了几个数据驱动的模型离线疲劳失效。然而，他们中的大多数都不能准确的实时的【形】疲劳预测和UQ。在目前的工作中，概率数据驱动的模型。提出了一种完全连接的人工神经网络（FC-ANN）和高斯过程回归（GPR）的混合结构，以确保增强的预测能力和同时UQ的预测TtF。利用合成数据和实验数据验证了该模型的实时的【形】预测和UQ性能。这种新的混合方法是完全数据驱动的，并扩展了现有的时域和基于机器学习的疲劳预测方法的预测能力。它为开发一种预防系统铺平了道路，该系统为结构健康监测（SHM）提供实时的【形】安全和操作指令和见解，从而防止环境破坏和人类生命损失。
英文摘要：
Various engineering systems such as naval and aerial vehicles, offshore structures, and mechanical components of motorized systems, are exposed to fatigue failures due to stochastic loadings. Methods for early failure prediction are essential for engineering, military, and civil applications. In addition to the prediction of time to failure (TtF), uncertainty quantification (UQ) is of major importance for real-time decision-making purposes. Usually, time domain or frequency domain methods are used for fatigue prediction, such as rainflow counting and Miner's rule or Dirlik's method. However, those methods suffer from over-simplistic modeling and inaccurate failure predictions under stochastic loadings. During the last years, several data-driven models were suggested for offline fatigue failure. However, most of them are not capable of both accurate real-time fatigue prediction and UQ. In the current work, a probabilistic data-driven model is introduced. A hybrid architecture of a fully-connected artificial neural network (FC-ANN) and Gaussian process regression (GPR) is proposed to ensure enhanced predictive abilities and simultaneous UQ of the predicted TtF. The real-time prediction and UQ performances of the suggested model are validated using both synthetic and experimental data. This novel hybrid method is fully data-driven and extends the forecasting capabilities of existing time-domain and machine learning-based methods for fatigue prediction. It paves the way towards the development of a preventive system that provides real-time safety and operational instructions and insights for structural health monitoring (SHM) purposes, allowing prevention of environmental damage, and loss of human lives.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
