VisioRed: A Visualisation Tool for Interpretable Predictive Maintenance
Spyridon Paraschos, Ioannis Mollas, Nick Bassiliades, Grigorios Tsoumakas
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.
BibTeX
@inproceedings{ijcai2021p713,
title = {VisioRed: A Visualisation Tool for Interpretable Predictive Maintenance},
author = {Paraschos, Spyridon and Mollas, Ioannis and Bassiliades, Nick and Tsoumakas, Grigorios},
booktitle = {Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, {IJCAI-21}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Zhi-Hua Zhou},
pages = {5004--5007},
year = {2021},
month = {8},
note = {Demo Track},
doi = {10.24963/ijcai.2021/713},
url = {https://doi.org/10.24963/ijcai.2021/713},
}