mlsanityData to 7 Oct 2026

VisioRed: A Visualisation Tool for Interpretable Predictive Maintenance

arXiv 2103.17003 ↗First posted 31 Mar 2021cs.LGcited by 2

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Abstract (arXiv)

The use of machine learning rapidly increases in high-risk scenarios where decisions are required, for example in healthcare or industrial monitoring equipment. In crucial situations, a model that can offer meaningful explanations of its decision-making is essential. In industrial facilities, the equipment's well-timed maintenance is vital to ensure continuous operation to prevent money loss. Using machine learning, predictive and prescriptive maintenance attempt to anticipate and prevent eventual system failures. This paper introduces a visualisation tool incorporating interpretations to display information derived from predictive maintenance models, trained on time-series data.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 6 of its 20 references that are in the map. First posted on arXiv 31 Mar 2021.

Builds on

6 of 20 references.

References · 6

  1. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  2. 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcited by 744
  3. 2020A Survey of Visual Analytics Techniques for Machine Learningcited by 56
  4. 2018Explainable time series tweaking via irreversible and reversible temporal transformationscited by 8
  5. 2020Altruist: Argumentative Explanations through Local Interpretations of Predictive Modelscited by 5
  6. 2019LioNets: Local Interpretation of Neural Networks through Penultimate Layer Decodingcited by 3

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Citing papers per year: 2021: 1; 2022: 1.

2 papers.