Failure-Averse Active Learning for Physics-Constrained Systems
Failure-Averse Active Learning for Physics-Constrained Systems
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DOI：
10.1109/tase.2022.3213827
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发表时间：
2021-10
影响因子：
5.6
通讯作者：
Cheolhei Lee;Xing Wang;Jianguo Wu;Xiaowei Yue
中科院分区：
文献类型：
--
作者：
Cheolhei Lee;Xing Wang;Jianguo Wu;Xiaowei Yue
主动学习是机器学习的一个子领域，是为设计和建模具有非常昂贵的采样成本的系统而设计的。工业和工程系统通常受到物理约束，当它们被违反时，可能会导致致命的故障，而这种约束在主动学习中经常被低估。在本文中，我们提出了一种新的主动学习方法，该方法考虑了控制系统的隐含物理约束，从而避免了失败。该方法由两个任务驱动：安全方差缩减探索安全区域以减小目标模型的方差，安全区域扩展旨在扩展可探索区域。集成采集功能旨在合并两个任务并明智地对其进行优化。将该方法应用于考虑材料失效的复合材料机身装配过程，在不知道显式失效区域的情况下，实现了零失效。给实践者的提示--这篇论文是由与系统故障相关的隐含物理约束的工程系统引起的。隐式物理约束是指失效过程中不存在显式解析形式，因此需要要求苛刻的数值模拟或真实实验来检查一个人的安全性。本文的主要目标是开发一种主动学习策略，通过最小化故障来安全地学习系统中的目标过程，而不需要进行初步的可靠性分析。所提出的方法主要针对故障条件未被彻底调查或不确定的实际系统。将该方法应用于飞机制造过程中复合材料机身变形的预测建模，通过考虑复合材料失效准则，实现了抽样时的零失效。
Active learning is a subfield of machine learning that is devised for the design and modeling of systems with highly expensive sampling costs. Industrial and engineering systems are generally subject to physics constraints that may induce fatal failures when they are violated, while such constraints are frequently underestimated in active learning. In this paper, we develop a novel active learning method that avoids failures considering implicit physics constraints that govern the system. The proposed approach is driven by two tasks: safe variance reduction explores the safe region to reduce the variance of the target model, and safe region expansion aims to extend the explorable region. The integrated acquisition function is devised to conflate two tasks and judiciously optimize them. The proposed method is applied to the composite fuselage assembly process with consideration of material failure using the Tsai-Wu criterion, and it is able to achieve zero failure without the knowledge of explicit failure regions. Note to Practitioners—This paper is motivated by engineering systems with implicit physics constraints related to system failures. Implicit physics constraints refer to failure processes in which explicit analytic forms do not exist, so demanding numerical simulations or real experiments are required to check one’s safety. The main objective of this paper is to develop an active learning strategy that safely learns the target process in the system by minimizing failures without preliminary reliability analysis. The proposed method mainly targets real systems whose failure conditions are not thoroughly investigated or uncertain. We applied the proposed method to the predictive modeling of composite fuselage deformation in the aircraft manufacturing process, and it achieved zero failure in sampling by considering the composite failure criterion.