A Hypergraph-Based Knowledge Representation Model for Smart Product-Service System Development

2021 ◽  
Author(s):  
Wang Zuoxu ◽  
Li Xinyu ◽  
Chen Chun-Hsien ◽  
Zheng Pai

Abstract In the trend of digital servitization, manufacturing companies have been transforming their business paradigms to Smart product-service systems (Smart PSS) by integrating products and associated services as bundles. To support the knowledge-intensive process of Smart PSS development, massive domain knowledge should be well-organized and reused. However, due to the existence of non-binary relations caused by product-service bundles (PSB) and context-awareness concerns in the Smart PSS development activities, conventional graph-based approaches for knowledge representation may lose essential information in transforming non-binary relations into binary ones, and hence cause incorrect results in the subsequent knowledge queries. To mitigate this problem, a hypergraph-based knowledge representation model for Smart PSS was proposed, which represents the non-binary relations among multiple entities with hyperedges. Technically, the knowledge source and the typical hyperedge schema in Smart PSS development are identified in this paper. A detailed case study in the scenarios of 3D printing troubleshooting and PSB recommendation was conducted to showcase the proposed hypergraph-based knowledge representation model and demonstrate its validity. The results show that the hypergraph-based knowledge model significantly relieves the sparsity in the ordinary KG by adding multiple hyperedges. It is anticipated that the proposed hypergraph knowledge representation model can serve as a fundamental study for further knowledge reasoning activities.

Author(s):  
Alessandro Umbrico ◽  
Gabriella Cortellessa ◽  
Andrea Orlandini ◽  
Amedeo Cesta

A key aspect of robotic assistants is their ability to contextualize their behavior according to different needs of assistive scenarios. This work presents an ontology-based knowledge representation and reasoning approach supporting the synthesis of personalized behavior of robotic assistants. It introduces an ontological model of health state and functioning of persons based on the International Classification of Functioning, Disability and Health. Moreover, it borrows the concepts of affordance and function from the literature of robotics and manufacturing and adapts them to robotic (physical and cognitive) assistance domain. Knowledge reasoning mechanisms are developed on top of the resulting ontological model to reason about stimulation capabilities of a robot and health state of a person in order to identify action opportunities and achieve personalized assistance. Experimental tests assess the performance of the proposed approach and its capability of dealing with different profiles and stimuli.


2021 ◽  
Vol 1 ◽  
pp. 81-90
Author(s):  
John Bake Sakwe ◽  
Marcus Pereira Pessoa ◽  
Sipke Hoekstra

AbstractWith the quest for enhancing competitive position, fulfilling customer and sustainability demands, increasing profitability, asset manufacturing companies are now adapting assets towards product service systems (PSS) offered through performance contracts. Despite several benefits, the shift to performance PSS exposes industrial asset manufacturers' to performance challenges and risks. Currently, PSS designers face a challenge to exhaustively identify potential failures during PSS development. Knowledge of Product failures is critical prior to the engineering of PSS. This paper proposes a failure modes and effects analysis (FMEA) method to support designers' prioritise critical failures in performance PSS development. A case study of an optical sorting machine is used to demonstrate the method's application.


2021 ◽  
Vol 48 ◽  
pp. 101310
Author(s):  
Guo Jia ◽  
Guiyi Zhang ◽  
Xin Yuan ◽  
Xiaosong Gu ◽  
Heshan Liu ◽  
...  

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