TechnoAlpin launches promising research project

Leading manufacturer of snowmaking equipment TechnoAlpin is embarking upon a research project with the Free University of Bozen-Bolzano and the Durst Group, Italian manufacturer of advanced digital printing and production technologies. The project, which is called “Premise” and is funded by the European Union ERDF, aims to build an infrastructure for predictive maintenance of production facilities.

Snowmaking systems are complex technological systems which are only used seasonally. The pumping stations and compressor plants are the heart of every system and consist of a large number of hydraulic, mechanical and electronic components. If faults occur here, the entire system can come to a standstill. TechnoAlpin has already been focusing for many years on the regular servicing of machine rooms as a means of preventing potential difficulties from arising. “Predictive maintenance enables us to build even more efficient and more reliable snowmaking systems as it allows the early detection of faults based on data history and real-time data,” explained Thomas Tschager, Team Leader Data Analytics at TechnoAlpin. “We will be analyzing data from our control software in this project in order to monitor the condition of the individual components in the machine rooms and to be able to predict impending failures. This will make service call-outs more efficient and easier to plan. This new technology can also show us the ways in which we might be able to optimize our machine rooms.”

Project "Premise" - European Union ERDF

The “Premise” project will be led by Prof. Johann Gamper from the Faculty of Computer Science and will run for two years. The aim of this project is to develop algorithms and methods for predictive maintenance. The Free University of Bozen-Bolzano has extensive experience in data analysis which will be used in this project. TechnoAlpin and Durst will be pooling their wealth of knowledge in the relevant field. The two companies apply predictive maintenance in different cases so this will allow various scenarios and algorithms to be developed more quickly. This should open the door to the development of a universal system with scope for easy incorporation of customized versions adapted to new environments. 

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