Recommendation function for smart data analytics toolbox to support semantic merging of middle-of-life data streams

Conference paper (2020)

Authors

F. Abou Eddahab-Burke Cyber-Physical Systems - IDE
I. Horvath Cyber-Physical Systems - IDE
Research Group
Cyber-Physical Systems (IDE) (TU Delft)
Copyright: © 2020 F. Abou Eddahab-Burke, I. Horvath
DOI
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https://doi.org/10.1109/ICCAD49821.2020.9260510
Data analytics Middle-of-life data Data merging Product designers Semantic interpretation White goods
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Published Date

2020

Language

English

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Faculty

Industrial Design Engineering

Department

Sustainable Design Engineering

Research Group

Cyber-Physical Systems

Abstract

Continuous enhancements of connected products make them able to generate and communicate a huge amounts of middle-of-life data streams to their producers. This affordance also creates a challenge for current data analytics tools unable to keep up with the heterogeneous nature and characteristics of these type of data. Accordingly, a function able to combine data from multiple data streams and analyze them as one source of information is definitely needed in a next-generation data analytics toolbox to support product enhancements by designers. As a result of a recent Ph.D. project, this paper presents the conceptualization and the implementation of a novel function of merging middle-of-life data streams. The implemented computational mechanism (i) acquires middle-of-life data streams, (ii) pre-processes them individually, (iii) merges information from the concerned streams, (iv) derives recommendation based on the merged information, and (v) send a recommendation as a message to the designer. The performance of the computational implementation was tested in an application case of data steaming and management to white goods designers for enhancing a connected washing machine. From a computational point of view, the testing proved that the set of proprietary algorithms designed for the realization of computational merging, together with the existing ones taken from the literature, were able to efficiently perform the subtasks. The advantages of merges were: (i) it provides more information than the one obtained by processing sensors' data individually, (ii) it reflects the condition of the product with a higher fidelity, (iii) it communicates information about the product while it is in use by the customer, (iv) it reduces the sensors analyses time and effort, and (v) it provides recommendation as an action plan concerning the product at hand. The outcomes of this study will be used in a follow up research to develop a comprehensive smart data analytics toolbox to support product designers in product innovation.