Information visualisation for industrial process monitoring

Elodie Toufaili, Christophe Bortolaso, Youssef Miloudi, Jean-Marc Petit, Vasile-Marian Scuturici

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.