Fleet Prognosis with Physics-informed Recurrent Neural Networks
Fleet Prognosis with Physics-informed Recurrent Neural Networks
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DOI：
10.12783/shm2019/32301
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发表时间：
2019-01
期刊：
影响因子：
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通讯作者：
R. Nascimento;F. Viana
中科院分区：
文献类型：
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作者：
R. Nascimento;F. Viana
大型工程资产的服务和保修是一项非常有利可图的业务。该领域公司的成功通常与高级分析驱动的预测性维护有关。因此，准确的建模是了解运行条件和组件功能之间的复杂相互作用如何定义使用寿命的一种方式，是服务盈利能力的关键。不幸的是，为大型机队建立预测模型是一项艰巨的任务，因为占空比变化、恶劣环境、维护不足以及大规模生产问题等因素可能会导致设计和观察到的使用寿命之间存在巨大差异。本文通过将循环神经网络扩展到累积损伤模型，介绍了一种新颖的物理信息神经网络预测方法。我们提出了一种新的循环神经网络单元，旨在合并物理信息层和数据驱动层。这样，工程师和科学家就有机会使用物理信息层对易于理解的零件（例如疲劳裂纹扩展）进行建模，并使用数据驱动层对特征较差的零件（例如内部载荷）进行建模。使用一个简单的数值实验来展示所提出的用于损伤累积的物理通知循环神经网络的主要特征。测试问题包括预测承受不同任务组合的合成飞机机队的疲劳裂纹长度。该模型使用完整的观测输入（远场载荷）和非常有限的输出观测（仅对一部分机队进行检查时的裂纹长度）进行训练。结果表明，即使观察到的裂纹长度分布与（不可观察的）舰队分布不匹配，我们提出的混合物理通知的循环神经网络也能够准确地模拟疲劳裂纹扩展。
Services and warranties of large fleets of engineering assets is a very profitable business. The success of companies in that area is often related to predictive maintenance driven by advanced analytics. Therefore, accurate modeling, as a way to understand how the complex interactions between operating conditions and component capability define useful life, is key for services profitability. Unfortunately, building prognosis models for large fleets is a daunting task as factors such as duty cycle variation, harsh environments, inadequate maintenance, and problems with mass production can lead to large discrepancies between designed and observed useful lives. This paper introduces a novel physics-informed neural network approach to prognosis by extending recurrent neural networks to cumulative damage models. We propose a new recurrent neural network cell designed to merge physics-informed and data-driven layers. With that, engineers and scientists have the chance to use physics-informed layers to model parts that are well understood (e.g., fatigue crack growth) and use data-driven layers to model parts that are poorly characterized (e.g., internal loads). A simple numerical experiment is used to present the main features of the proposed physics-informed recurrent neural network for damage accumulation. The test problem consist of predicting fatigue crack length for a synthetic fleet of airplanes subject to different mission mixes. The model is trained using full observation inputs (far-field loads) and very limited observation of outputs (crack length at inspection for only a portion of the fleet). The results demonstrate that our proposed hybrid physics-informed recurrent neural network is able to accurately model fatigue crack growth even when the observed distribution of crack length does not match with the (unobservable) fleet distribution.