VisioRed: A Visualisation Tool for Interpretable Predictive Maintenance

Paraschos, Spyridon; Mollas, Ioannis; Bassiliades, Nick; Tsoumakas, Grigorios

doi:10.24963/ijcai.2021/713

Cited by 3 publications

(1 citation statement)

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“…In [6], the authors propose VisioRed, a visualisation tool for interpretable predictive maintenance. This system is compatible with multi-variate time-series data, and its end product is the prediction of a component's remaining useful lifetime (RUL).…”

Section: Related Workmentioning

confidence: 99%

DeepTimeAnomalyViz: A Tool for Visualizing and Post-processing Deep Learning Anomaly Detection Results for Industrial Time-Series

Leporowski¹,

Hansen²,

Iosifidis³

2021

Preprint

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Industrial processes are monitored by a large number of various sensors that produce time-series data. Deep Learning offers a possibility to create anomaly detection methods that can aid in preventing malfunctions and increasing efficiency. But creating such a solution can be a complicated task, with factors such as inference speed, amount of available data, number of sensors, and many more, influencing the feasibility of such implementation. We introduce the DeTAVIZ interface, which is a web browser based visualization tool for quick exploration and assessment of feasibility of DL based anomaly detection in a given problem. Provided with a pool of pretrained models and simulation results, DeTAVIZ allows the user to easily and quickly iterate through multiple post processing options and compare different models, and allows for manual optimisation towards a chosen metric.

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Section: Related Workmentioning

confidence: 99%

DeepTimeAnomalyViz: A Tool for Visualizing and Post-processing Deep Learning Anomaly Detection Results for Industrial Time-Series

Leporowski¹,

Hansen²,

Iosifidis³

2021

Preprint

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Truthful meta-explanations for local interpretability of machine learning models

Mollas,

Bassiliades,

Tsoumakas

2023

Appl Intell

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Automated Machine Learning-based systems’ integration into a wide range of tasks has expanded as a result of their performance and speed. Although there are numerous advantages to employing ML-based systems, if they are not interpretable, they should not be used in critical or high-risk applications. To address this issue, researchers and businesses have been focusing on finding ways to improve the explainability of complex ML systems, and several such methods have been developed. Indeed, there are so many developed techniques that it is difficult for practitioners to choose the best among them for their applications, even when using evaluation metrics. As a result, the demand for a selection tool, a meta-explanation technique based on a high-quality evaluation metric, is apparent. In this paper, we present a local meta-explanation technique which builds on top of the truthfulness metric, which is a faithfulness-based metric. We demonstrate the effectiveness of both the technique and the metric by concretely defining all the concepts and through experimentation.

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Tackling Industrial Downtimes with Artificial Intelligence in Data-Driven Maintenance

Hoffmann,

Lasch

2023

ACM Comput. Surv.

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The application of Artificial Intelligence (AI) approaches in industrial maintenance for fault detection and prediction has gained much attention from scholars and practitioners. This survey systematically assesses and classifies the state-of-the-art algorithms applied to data-driven maintenance in recent literature. The taxonomy provides a so far not existing overview and decision aid for research and practice regarding suitable AI approaches for each maintenance application. Moreover, we consider trends and further research demand in this area. Finally, a newly developed holistic maintenance framework contributes to a practice-oriented implementation of AI and considers crucial managerial aspects of an efficient maintenance system.

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VisioRed: A Visualisation Tool for Interpretable Predictive Maintenance

Cited by 3 publications

References 0 publications

DeepTimeAnomalyViz: A Tool for Visualizing and Post-processing Deep Learning Anomaly Detection Results for Industrial Time-Series

DeepTimeAnomalyViz: A Tool for Visualizing and Post-processing Deep Learning Anomaly Detection Results for Industrial Time-Series

Truthful meta-explanations for local interpretability of machine learning models

Tackling Industrial Downtimes with Artificial Intelligence in Data-Driven Maintenance

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