International Conference on Information Visualization for Industrial Process Monitoring (Greece)

Maintenance activity.

On May 6, in the city of Heraklion (Greece), on the occasion of IDEAS 2023, the 27th International Symposium on Database Engineering Applications, Elodie Toufaili from the BL.Predict team will hold a conference entitled: “Information visualization for industrial process monitoring”.

This contribution is the result of her current thesis work on the definition of a decision support platform for maintenance, in collaboration with the Laboratoire d’InfoRmatique en Image et Systèmes d’information (UMR 5205) in the Lyon area.

Its main objective is to optimize the performance of industrial processes by providing a visual analysis tool of the operating status of equipment that is relevant and easily understandable for maintenance engineers. To do this, it seeks to represent data from multivariate time series by means of co-occurrence matrices, so as to visualize the state of an equipment and prevent possible breakdowns before they become too critical.

An innovation to making the diagnostic assistance more reliable!

Its innovation? The definition of criteria allowing to choose the set of parameters that best inform the state of the studied equipment through the massive flow of continuously generated data. This selection allows the creation of customizable matrices adapted to the various problems of each industry, thus making the diagnostic assistance more reliable.

Let’s take the example of the baggage sorting system at Lyon Saint Exupéry airport (2020 data set, Figure 1). Conveyors are equipped with sensors that measure, among other things, the temperature of the motor, the speed of the conveyor belt, and the weight of the luggage. The visualization below shows, for example, a correlation between an increase in conveyor temperature and an increased frequency of baggage jams.

Figure 1: Visualization of operating problems of equipment integrated into the baggage sorting system at Lyon airport (2020 data)

This initial work is part of a plan to enhance the features of BL.Predict, an interoperable AI/IoT platform developed by CARL Berger-Levrault to support both industry and public organizations in the sustainable management of their equipment and infrastructure.

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