Information visualisation for industrial process monitoring
Authors: 
Elodie Toufaili, Christophe Bortolaso, Youssef Miloudi, 
Jean-Marc Petit, Vasile-Marian ScuturiciAuthors Info & Claims
Pages 107 - 114
Abstract
In the context of process monitoring and predictive maintenance, an adapted visualisation of sensor data is essential in order to help the domain experts to make the right maintenance decision. The large volume and diversity of data leads us to aggregate the data to obtain semantically rich information useful to the domain expert. We study the case of industrial machinery equipped with several sensors producing time series, and we consider that this machinery has different operating states in its operation. We propose a method to identify an optimal representation of the data in 2 dimensions, understandable by the domain expert. This representation allows to easily identify the operating modes of the equipment and the possible deviation from a "normal" behavior. We use co-occurrence matrices to synthesise the time series data, and the features of interest and discretization are selected using two proposed criteria to measure the separation of working modes.
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Thyago P. Carvalho 2019. A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering 137 (Nov. 2019).
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Benjamin Chazelle, Pierre-Loic Maisonneuve, Ammar Mechouche, Jean-Marc Petit, and Vasile-Marian Scuturici. 2021. From Large Time Series to Patterns Movies: Application to Airbus Helicopters Flight Data. In Advances in Databases and Information Systems. Vol. 12843. Cham, 213–226.
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Index Terms
- Information visualisation for industrial process monitoring
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Published In
May 2023
222 pages
ISBN:9798400707445
DOI:10.1145/3589462
Copyright © 2023 ACM.
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Association for Computing Machinery
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Published: 26 May 2023
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IDEAS '23
IDEAS '23: International Database Engineered Applications Symposium Conference
May 5 - 7, 2023
Heraklion, Crete, Greece
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Overall Acceptance Rate 74 of 210 submissions, 35%
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