Decision support for multi-component systems: visualizing interdependencies for predictive maintenance

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

Taking dependencies between components seriously and considering the multi-component perspective instead of the single-system perspective could help to improve the results of predictive maintenance (PdM). However, modeling and identifying the interdependencies in complex industrial systems is challenging. A way to tackle this challenge and to identify interdependencies is using visualization. To the best of our knowledge, existing research on visualizing interdependencies is not applied to multi-component systems (MCS) so far. Further, it is not clear how visualization approaches can provide suitable decision support to identify interdependencies in PdM tasks. We evaluate three key visualization approaches to represent interdependencies in the context of PdM for MCS using a crowd-sourced design study in a questionnaire survey involving 530 participants. Based on our study, we were able to rank these approaches based on performance and usability for our given PdM task. The multi-line approach outperformed other approaches with respect to performance.
Original languageEnglish
Title of host publicationProceedings of the 55th Hawaii International Conference on System Sciences
Number of pages10
DOIs
Publication statusPublished - 4 Jan 2022
Event55th Annual Hawaii International Conference on System Sciences: HICSS 2022 - Virtuell, United States
Duration: 3 Jan 20227 Jan 2022
https://hicss.hawaii.edu/

Conference

Conference55th Annual Hawaii International Conference on System Sciences
Abbreviated titleHICSS 2022
Country/TerritoryUnited States
CityVirtuell
Period3/01/227/01/22
Internet address

Keywords

  • Interactive Visual Analytics and Visualization for Decision Making
  • visualization
  • predictive maintenance
  • multi-component systems
  • interdependencies
  • decision support

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