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所属单位：机电学院
发表刊物：INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
关键字：Internet of Things manufacturing system frequent trajectory data mining work-in-process
摘要：The application of Internet of Things technologies has led to a data-rich manufacturing environment by connecting manufacturing objects as a collaborative community. However, advanced analytics approach is comparatively inadequate for work-in-process (WIP) trajectory data. On the other hand, although the topic of mining frequent trajectory patterns has raised a great deal of attention, it mainly focuses on the fields of vehicle traffic management and users' behaviours. When applied in manufacturing shop floor, the extracted knowledge is physical trajectory patterns and lacks manufacturing significance. This paper manages to obtain logical knowledge with manufacturing significance from WIP trajectory data. In this paper, a data model is introduced to map physical trajectories of WIP into logical space, in order to capture logical features of manufacturing system. Moreover, an algorithm named PMP is proposed to extract logical trajectory patterns. Several experiments are conducted to examine the performance. The results prove the efficiency and feasibility of the proposed method.
ISSN号：0951-192X
是否译文：否
发表时间：2017-01-01
合写作者：Cai, Haoshu,杨文安,Lu, Kun
通讯作者：郭宇