SMILE: Smart Monitoring IoT Learning Ecosystem

Roberta Avanzato, Francesco Beritelli, Francesco Di Franco, Michele Russo


In industrial contexts to date, there are several solutions to monitor and intervene in case of anomalies and/or failures. Using a classic approach to cover all the requirements needed in the industrial field, different solutions should be implemented for different monitoring platforms, covering the required end-to-end. The classic cause-effect association process in the field of industrial monitoring requires thorough understanding of the monitored ecosystem and the main characteristics triggering the detected anomalies. In these cases, complex decision-making systems are in place often providing poor results. This paper introduces a new approach based on an innovative industrial monitoring platform, which has been denominated SMILE. It allows offering an automatic service of global modern industry performance monitoring, giving the possibility to create, by setting goals, its own machine/deep learning models through a web dashboard from which one can view the collected data and the produced results.  Thanks to an unsupervised approach the SMILE platform can understand which the linear and non-linear correlations are representing the overall state of the system to predict and, therefore, report abnormal behavior.


unsupervised machine learning; industry 4.0; smart monitoring; internet of things; maintenance perspective.

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