A Visual Analytics Approach for the Diagnosis of Heterogeneous and Multidimensional Machine Maintenance Data

Takanori Fujiwara, Senthil Chandrasegaran, Michael P. Brundage, Thurston Sexton, Alden Dima, Kwan Liu Ma

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Analysis of large, high-dimensional, and heterogeneous datasets is challenging as no one technique is suitable for visualizing and clustering such data in order to make sense of the underlying information. For instance, heterogeneous logs detailing machine repair and maintenance in an organization often need to be analyzed to diagnose errors and identify abnormal patterns, formalize root-cause analyses, and plan preventive maintenance. Such real-world datasets are also beset by issues such as inconsistent and/or missing entries. To conduct an effective diagnosis, it is important to extract and understand patterns from the data with support from analytic algorithms (e.g., finding that certain kinds of machine complaints occur more in the summer) while involving the human-in-the-loop. To address these challenges, we adopt existing techniques for dimensionality reduction (DR) and clustering of numerical, categorical, and text data dimensions, and introduce a visual analytics approach that uses multiple coordinated views to connect DR + clustering results across each kind of the data dimension stated. To help analysts label the clusters, each clustering view is supplemented with techniques and visualizations that contrast a cluster of interest with the rest of the dataset. Our approach assists analysts to make sense of machine maintenance logs and their errors. Then the gained insights help them carry out preventive maintenance. We illustrate and evaluate our approach through use cases and expert studies respectively, and discuss generalization of the approach to other heterogeneous data.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE 14th Pacific Visualization Symposium, PacificVis 2021
PublisherIEEE
Pages196-205
Number of pages10
ISBN (Electronic)9781665439312
DOIs
Publication statusPublished - Apr 2021
Event14th IEEE Pacific Visualization Symposium, PacificVis 2021 - Virtual, Tianjin, China
Duration: 19 Apr 202122 Apr 2021

Publication series

NameIEEE Pacific Visualization Symposium
Volume2021-April
ISSN (Print)2165-8765
ISSN (Electronic)2165-8773

Conference

Conference14th IEEE Pacific Visualization Symposium, PacificVis 2021
CountryChina
CityVirtual, Tianjin
Period19/04/2122/04/21

Keywords

  • heterogeneous data
  • high-dimensional data
  • machine learning
  • maintenance logs
  • text analytics
  • Visual analytics

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