---
title: "VisioRed: A Visualisation Tool for Interpretable Predictive Maintenance"
authors:
  - "Spyridon Paraschos"
  - "Ioannis Mollas"
  - "Nick Bassiliades"
  - "Grigorios Tsoumakas"
doi: "10.24963/ijcai.2021/713"
url: "https://ijcai.org/proceedings/2021/713"
pdf: "https://ijcai.org/proceedings/2021/0713.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "5004-5007"
track: "Demo Track"
keywords:
  - "Machine Learning: General"
  - "Human-Computer Interaction: General"
---

# VisioRed: A Visualisation Tool for Interpretable Predictive Maintenance

Spyridon Paraschos, Ioannis Mollas, Nick Bassiliades, Grigorios Tsoumakas

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 5004-5007. DOI: [10.24963/ijcai.2021/713](https://doi.org/10.24963/ijcai.2021/713)

## Abstract

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.