# VisioRed: A Visualisation Tool for Interpretable Predictive Maintenance

arXiv: https://arxiv.org/abs/2103.17003 · HTML: https://mlsanity.com/paper/2103.17003/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 31 Mar 2021.

## Builds on (most cited first, 6 of 6)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [A Survey of Visual Analytics Techniques for Machine Learning](https://mlsanity.com/paper/2008.09632/) (2020)
- [Explainable time series tweaking via irreversible and reversible temporal transformations](https://mlsanity.com/paper/1809.05183/) (2018)
- [Altruist: Argumentative Explanations through Local Interpretations of Predictive Models](https://mlsanity.com/paper/2010.07650/) (2020)
- [LioNets: Local Interpretation of Neural Networks through Penultimate Layer Decoding](https://mlsanity.com/paper/1906.06566/) (2019)

## Built on by (most cited first, 2 of 2)

- [Truthful Meta-Explanations for Local Interpretability of Machine Learning Models](https://mlsanity.com/paper/2212.03513/) (2022)
- [DeepTimeAnomalyViz: A Tool for Visualizing and Post-processing Deep Learning Anomaly Detection Results for Industrial Time-Series](https://mlsanity.com/paper/2109.10082/) (2021)

## 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.