Privacy-preserving for assembly deviation prediction in a machine learning model of hydraulic equipment under value chain collaboration.

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  • Additional Information
    • Source:
      Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
    • Publication Information:
      Original Publication: London : Nature Publishing Group, copyright 2011-
    • Subject Terms:
      Machine Learning* ; Privacy*
    • Abstract:
      Hydraulic equipment, as a typical mechanical product, has been wildly used in various fields. Accurate acquisition and secure transmission of assembly deviation data are the most critical issues for hydraulic equipment manufacturer in the PLM-oriented value chain collaboration. Existing deviation prediction methods are mainly used for assembly quality control, which concentrate in the product design and assembly stage. However, the actual assembly deviations generated in the service stage can be used to guide the equipment maintenance and tolerance design. In this paper, a high-fidelity prediction and privacy-preserving method is proposed based on the observable assembly deviations. A hierarchical graph attention network (HGAT) is established to predict the assembly feature deviations. The hierarchical generalized representation and differential privacy reconstruction techniques are also introduced to generate the graph attention network model for assembly deviation privacy-preserving. A derivation gradient matrix is established to calculate the defined modified necessary index of assembly parts. Two privacy-preserving strategies are designed to protect the assembly privacy of node representation and adjacent relationship. The effectiveness and superiority of the proposed method are demonstrated by a case study with a four-column hydraulic press.
      (© 2022. The Author(s).)
    • References:
      PLoS One. 2019 Nov 7;14(11):e0224365. (PMID: 31697686)
      PLoS One. 2020 Jan 24;15(1):e0227804. (PMID: 31978150)
    • Publication Date:
      Date Created: 20220624 Date Completed: 20220628 Latest Revision: 20220829
    • Publication Date:
      20221213
    • Accession Number:
      PMC9232523
    • Accession Number:
      10.1038/s41598-022-14835-1
    • Accession Number:
      35750710