Rolls-royce Deutschland Ltd & Co. Kg

Hoerbiger Automatisierungstechnik Holding Gmbh

Shankar Deepak Srinivasan

Jochen Schaible

Martin Trittler

Klaus Paul

Andreas Stückle

Shri Nishanth Rajendran

Simon Mösslang

Astrid Walle

The invention relates to a method for training machine learning models, having the steps of: detecting data in the form of time series data using one or more computers, said data being obtained by means of one or more measuring devices (-), in each case in the form of a sensor for measuring a physical variable; receiving multiple classification data units relating to the data using the one or more computers; receiving a selected part of the data using the one or more computers for each of the classification data units; and training multiple machine learning models using the one or more computers, in each case on the basis of at least one of the classification data units and the at least one corresponding selected part of the data, wherein the multiple machine learning models represent multiple instances of the same machine learning model.

Training of machine learning models for data-driven decision-making

A machine press is provided in which at least one hermetically sealed hydraulic drive unit is switchable from a working mode, in which a base pressure above ambient pressure is continually exceeded, into a rest mode. To this end, in parallel with a pressure converter, to the high-pressure side of which a pressure store and a raising working chamber of a cylinder piston unit are connected, a bypass is provided, having a first blocking valve that is controllable by a control unit and, in its blocking position, blocks the through-flow direction from the high-pressure side of the pressure converter to the low-pressure side. Furthermore, the low-pressure side is connectable to a lowering working chamber via a second blocking valve controllable by the control unit. The pressure output of the hydraulic unit is connectable to the raising working chamber via a third blocking valve controllable by the control unit.

Machine press

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Berlin, Germany

Shankar Deepak Srinivasan