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Karolingerstr. 7b
5020 Salzburg

Phone / Fax: show
Phone: +43 662 4310020
Fax: +43 662 43100233
InnoTrans 2018
  • Public Transport Public Transport

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Product description

From Sensors to the Cloud: smart software solutions in railway
In industrial environments cloud computing is primarily suited to data-intensive applications and vertical data integration. Equipment data is aggregated, sent to the cloud and can be evaluated and compared there optimally. Applications for this range from company-wide dashboards and benchmarks, through energy data management, to predictive analytics.
Predictive maintenance is a future-oriented concept which can already be productively implemented today. Predictive maintenance offers a forecast into the future of equipment or its parts. Predictions are made when equipment should be serviced or a component switched, based on data gained from experience and learning models. Data is continuously collected in running production and is analyzed. The system is thereby constantly learning and developing and allows interpretation of live data via a model. Rigorous maintenance cycles can thereby be replaced with individual service times for each component. The analysis and creation of the models can take place directly on-premise at the company or via a service in the cloud.
zenon is very well suited –  such as in connection with Azure Machine Learning from Microsoft – for a Predictive Maintenance System. The model generated with Azure is displayed by zenon in the SCADA system and the corresponding data appears on the HMI level.
Maintenance at the right moment offers a number of advantages compared to having a fixed cycle. Above all: Equipment can be maintained according to its usage. Increased loads will reduce maintenance periods and thereby prevent damage. Less load application will postpone the time of maintenance and thereby save unnecessary costs. Spare parts can thereby be ordered at the right time and in the right quantity and engineering time can be optimally planned.