@article {DEVITA202030,
	title = {On the use of a full stack hardware/software infrastructure for sensor data fusion and fault prediction in industry 4.0},
	journal = {Pattern Recognition Letters},
	volume = {138},
	year = {2020},
	pages = {30 - 37},
	abstract = {Aspects related to prognostics are becoming a crucial part in the industrial sector. In this sense, Industry 4.0 is considered as a new paradigm that leverages on the IoT to propose increasingly more solutions to provide an estimate on the working conditions of an industrial plant. However, in context like the industrial sector where the number and heterogeneity of sensors can be very large, and the time requirements are very stringent, emerges the challenge to design effective infrastructures to interact with these complex systems. In this paper, we propose a full stack hardware/software infrastructure to collect, manage, and analyze the data gathered from a set of heterogeneous sensors attached to a real scale replica industrial plant available in our laboratory. On top of the proposed infrastructure we designed and implemented a fault prediction algorithm which exploits sensors data fusion with the aim to assess the working conditions of the industrial plant. The result section shows the obtained results in terms of accuracy from testing our proposed model and provides a comparison with a traditional Deep Neural Network (DNN) topology.},
	keywords = {Data fusion, Deep Learning, IIoT industrial testbed, Industry4.0},
	issn = {0167-8655},
	doi = {https://doi.org/10.1016/j.patrec.2020.06.028},
	url = {http://www.sciencedirect.com/science/article/pii/S0167865520302464},
	author = {Fabrizio [De Vita] and Dario Bruneo and Sajal K. Das}
}
