Computer Engineering Department, San Jose' State University, San Jose, CA 95192, USA.
Department of Aerospace Engineering, San Jose' State University, San Jose, CA 95192, USA.
Sensors (Basel). 2021 Feb 27;21(5):1654. doi: 10.3390/s21051654.
Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.
目前，机械系统的维护间隔是根据系统寿命预先安排的，这导致了昂贵的维护计划，并且经常危及乘客的安全。今后，将使用车辆的实际使用情况来预测其结构中的应力，从而定义特定的维护计划。机器学习 (ML) 算法可用于将来自结构实时测量的一组简化数据映射到同一系统的详细/高保真有限元分析 (FEA) 模型。结果，基于 FEA 的 ML 方法将直接估计整个系统在运行过程中的应力分布，从而提高定义特定、安全和高效维护程序的能力。本文首先介绍了应用于有限元的最新 ML 方法的综述。还提出了一种基于 ML 算法的替代有限元方法来估计一维梁的时变响应。已经开发了几种 ML 回归模型，例如决策树和人工神经网络，并比较了它们的性能，以直接估计梁结构上的应力分布。基于 ML 算法的替代有限元模型能够准确地估计梁的响应，人工神经网络提供更准确的结果。
