Scalable ontology-based V&V process for heterogeneous systems and applications

Romain Delabeye, O. Penas, R. Plateaux
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引用次数: 1

Abstract

This work focuses on ongoing research within the EU-funded EnerMan project aiming at improving the energy efficiency of manufacturing systems. Industrial use cases are generally too constrained to easily proceed to the verification and validation (V&V) of the scientific approaches tackling their challenges. In this context, we propose an ontology-based framework with a methodology assessing the scalability of heterogeneous systems, environments, and missions in a V&V context. Indeed, projecting these industrial and laboratory applications onto a meaningful ontology allows them to be flattened out to the same scale from a semantic point of view. Reasoning is used to evaluate the extent to which a given scientific approach can be verified on a laboratory use case different from the industrial scenario on which it has to be validated. The framework has been implemented using Protégé and Owlready2, and applied to a scientific approach focused on a blind source separation technique used to identify system operating modes in a black box manner, tested on a coffee machine and two industrial case studies (a vehicle testbed's heating ventilation and air conditioning system, and a chocolate production line).
针对异构系统和应用程序的基于本体的可伸缩V&V流程
这项工作集中在欧盟资助的EnerMan项目中正在进行的研究,旨在提高制造系统的能源效率。工业用例通常过于受限,无法轻松地进行科学方法的验证和确认(V&V),以应对它们的挑战。在这种情况下,我们提出了一个基于本体的框架,该框架采用了一种方法来评估V&V环境中异构系统、环境和任务的可扩展性。事实上,将这些工业和实验室应用投射到一个有意义的本体上,可以从语义的角度将它们平展到相同的规模。推理用于评估给定的科学方法在实验室用例上可以验证的程度,而在工业场景上则必须进行验证。该框架已使用prot和Owlready2实现,并应用于一种科学方法,侧重于盲源分离技术,该技术用于以黑盒方式识别系统运行模式,并在一台咖啡机和两个工业案例研究(车辆试验台的加热通风和空调系统,以及巧克力生产线)上进行了测试。
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