基于不断发展的知识库的工业4.0状态监测语义框架

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Semantic Web Pub Date : 2023-10-05 DOI:10.3233/sw-233481
Franco Giustozzi, Julien Saunier, Cecilia Zanni-Merk
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引用次数: 1

摘要

在工业4.0中,工厂资产和机器配备了传感器,可以收集数据以进行有效的状态监测。这是一项困难的任务,因为它需要集成和处理来自不同来源的异构数据,这些数据具有不同的时间分辨率和潜在含义。本体论已经成为处理数据集成和通过构建语义模型以机器可解释的方式表示制造知识的相关方法。本体用于构建知识库中的知识,知识库还包含有关这些数据的实例和信息。因此,知识库提供了制造过程中涉及的不同元素的一种虚拟表示。此外,工业过程的监测取决于其执行的动态环境。在这些情况下,语义模型必须提供一种表示这种演变的方法,以便表示资源在执行其支持决策的任务期间所处的情况。本文提出了一个语义框架来解决工业4.0中状态监测知识库的演变问题。为此,我们首先为制造领域提出了一个语义模型(COInd4本体),该模型表示作为工厂一部分的资源和过程,特别强调这些资源和过程的上下文。将传感器观测与领域知识相结合的相关情况也在模型中表示。其次,介绍了一种使用流推理来检测导致潜在故障的情况的方法。该方法使用所提出的语义模型丰富了从传感器收集的具有上下文信息的数据。流推理的使用有助于不同数据源、不同时间分辨率的数据的集成以及对这些数据的实时处理。这允许从低级上下文和传感器信息派生高级情况。检测情况可以触发调整流程行为的操作,反过来,这种行为的更改可以导致生成导致新情况的新上下文。这些情况可以具有不同的严重程度,并且可以以不同的方式嵌套。处理各种情况之间丰富的关系需要一种有效的方法来组织它们。因此,我们提出了一种建立格的方法,根据它们所依赖的约束对这些情况进行排序。这个点阵表示了从给定的一个点阵可以到达的所有情况(正常或异常)的路线图。这有助于决策支持,通过允许识别可以采取的行动来纠正异常,避免以这种方式中断生产过程。最后,描述了该方法的工业应用场景。
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A semantic framework for condition monitoring in Industry 4.0 based on evolving knowledge bases
In Industry 4.0, factory assets and machines are equipped with sensors that collect data for effective condition monitoring. This is a difficult task since it requires the integration and processing of heterogeneous data from different sources, with different temporal resolutions and underlying meanings. Ontologies have emerged as a pertinent method to deal with data integration and to represent manufacturing knowledge in a machine-interpretable way through the construction of semantic models. Ontologies are used to structure knowledge in knowledge bases, which also contain instances and information about these data. Thus, a knowledge base provides a sort of virtual representation of the different elements involved in a manufacturing process. Moreover, the monitoring of industrial processes depends on the dynamic context of their execution. Under these circumstances, the semantic model must provide a way to represent this evolution in order to represent in which situation(s) a resource is in during the execution of its tasks to support decision making. This paper proposes a semantic framework to address the evolution of knowledge bases for condition monitoring in Industry 4.0. To this end, firstly we propose a semantic model (the COInd4 ontology) for the manufacturing domain that represents the resources and processes that are part of a factory, with special emphasis on the context of these resources and processes. Relevant situations that combine sensor observations with domain knowledge are also represented in the model. Secondly, an approach that uses stream reasoning to detect these situations that lead to potential failures is introduced. This approach enriches data collected from sensors with contextual information using the proposed semantic model. The use of stream reasoning facilitates the integration of data from different data sources, different temporal resolutions as well as the processing of these data in real time. This allows to derive high-level situations from lower-level context and sensor information. Detecting situations can trigger actions to adapt the process behavior, and in turn, this change in behavior can lead to the generation of new contexts leading to new situations. These situations can have different levels of severity, and can be nested in different ways. Dealing with the rich relations among situations requires an efficient approach to organize them. Therefore, we propose a method to build a lattice, ordering those situations depending on the constraints they rely on. This lattice represents a road-map of all the situations that can be reached from a given one, normal or abnormal. This helps in decision support, by allowing the identification of the actions that can be taken to correct the abnormality avoiding in this way the interruption of the manufacturing processes. Finally, an industrial application scenario for the proposed approach is described.
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来源期刊
Semantic Web
Semantic Web COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
8.30
自引率
6.70%
发文量
68
期刊介绍: The journal Semantic Web – Interoperability, Usability, Applicability brings together researchers from various fields which share the vision and need for more effective and meaningful ways to share information across agents and services on the future internet and elsewhere. As such, Semantic Web technologies shall support the seamless integration of data, on-the-fly composition and interoperation of Web services, as well as more intuitive search engines. The semantics – or meaning – of information, however, cannot be defined without a context, which makes personalization, trust, and provenance core topics for Semantic Web research. New retrieval paradigms, user interfaces, and visualization techniques have to unleash the power of the Semantic Web and at the same time hide its complexity from the user. Based on this vision, the journal welcomes contributions ranging from theoretical and foundational research over methods and tools to descriptions of concrete ontologies and applications in all areas. We especially welcome papers which add a social, spatial, and temporal dimension to Semantic Web research, as well as application-oriented papers making use of formal semantics.
期刊最新文献
Wikidata subsetting: Approaches, tools, and evaluation An ontology of 3D environment where a simulated manipulation task takes place (ENVON) Sem@ K: Is my knowledge graph embedding model semantic-aware? Using semantic story maps to describe a territory beyond its map NeuSyRE: Neuro-symbolic visual understanding and reasoning framework based on scene graph enrichment
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