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Predictions-on-chip: model-based training and automated deployment of machine learning models at runtime
Software and Systems Modeling ( IF 2 ) Pub Date : 2021-03-02 , DOI: 10.1007/s10270-020-00856-9
Sebastian Pilarski , Martin Staniszewski , Matthew Bryan , Frederic Villeneuve , Dániel Varró

The design of gas turbines is a challenging area of cyber-physical systems where complex model-based simulations across multiple disciplines (e.g., performance, aerothermal) drive the design process. As a result, a continuously increasing amount of data is derived during system design. Finding new insights in such data by exploiting various machine learning (ML) techniques is a promising industrial trend since better predictions based on real data result in substantial product quality improvements and cost reduction. This paper presents a method that generates data from multi-paradigm simulation tools, develops and trains ML models for prediction, and deploys such prediction models into an active control system operating at runtime with limited computational power. We explore the replacement of existing traditional prediction modules with ML counterparts with different architectures. We validate the effectiveness of various ML models in the context of three (real) gas turbine bearings using over 150,000 data points for training, validation, and testing. We introduce code generation techniques for automated deployment of neural network models to industrial off-the-shelf programmable logic controllers.



中文翻译:

片上预测:在运行时基于模型的训练和机器学习模型的自动部署

燃气轮机的设计是网络物理系统的一个充满挑战的领域,在该领域中,跨多个学科(例如性能,空气热学)的基于模型的复杂模拟驱动了设计过程。结果,在系统设计期间得出了不断增加的数据量。通过利用各种机器学习(ML)技术在此类数据中找到新见解是一种有前途的工业趋势,因为基于真实数据的更好预测可以显着提高产品质量并降低成本。本文提出了一种从多范式仿真工具生成数据,开发和训练用于预测的ML模型并将这种预测模型部署到运行时且运算能力有限的主动控制系统中的方法。我们探索用具有不同架构的ML对应物替换现有的传统预测模块。我们使用超过150,000个数据点进行培训,验证和测试,在三个(实际)燃气轮机轴承的背景下验证了各种ML模型的有效性。我们介绍了用于将神经网络模型自动部署到工业现成的可编程逻辑控制器的代码生成技术。

更新日期:2021-03-02
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