The system developed by Elmodis enables the monitoring and diagnostics of machinery, with the primary goal of predicting failures. Anomalies in the operation of the equipment are detected using electric measurements and innovative machine learning algorithms. The key feature of such systems is their ability to collect, link and forward data obtained in this manner relating to the operation of machinery. The system makes it easier to make correct maintenance decisions for the machine park. It is also of key importance for the units of Jastrzębska Spółka Węglowa, which must meet the challenges of the innovative industry 4.0.

- We want the technology supplied by our new partner to work effectively also in JSW's units - said Daniel Ozon, president of the JSW SA Management Board. - This is an excellent way to achieve savings in the operation of machinery.

The solution provided by Elmodis is a complete system that monitors the status of machinery on a 24/7 basis. It helps prevents unplanned downtime, reduces the costs of operation of machinery and as a result ensures greater availability of the whole machine park. Also, the system does not require any intervention in the installation and construction of the machinery.

- We want the people, the machines and the IT systems to automatically exchange information in the entire coal production process, within all the JSW S.A. units and key IT systems implemented in the JSW Group - said Piotr Toś, President of the Advicom Sp. z o.o. Management Board. - Also, the system should allow for a much easier and faster early detection of anomalies in the operation of machines, effectively helping us prevent unexpected breakdowns.

The Elmodis system has been developed by a group of Polish engineers with academic background. It is now time for a series of meetings to prepare the implementation of the system for selected machinery and equipment at JSW.

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JSW - Jastrzebska Spólka Weglowa SA published this content on 20 November 2018 and is solely responsible for the information contained herein. Distributed by Public, unedited and unaltered, on 20 November 2018 12:30:08 UTC