Hitachi Energy Ltd

Yaman Cansin Evrenosoglu

Ioannis Lymperopoulos

Adamantios Marinakis

Jan Poland

Pawel Dawidowski

Sinisa Zubic

Machine learning, ML, using labeled data is performed to thereby train a ML model to generate decision logic for a controller of an IACS, in particular a power system controller. The ML model has ML model inputs and ML model outputs. The ML model is decomposed into a plurality of computational sub-processes, wherein intermediate signals output by one of the computational sub-processes are input into a consecutive one of the computational sub-processes. Information on the trained ML model is generated based on the intermediate signals and is output for interpretation, verification, visualization, and/or inspection of at least one of the computational sub-processes.

Method and computer system for generating a decision logic for a controller

Performance of a decision-making logic of a controller of an industrial automation control system is monitored during field operation of the controller. A supervisory system receives operational data collected during field operation of the controller. The supervisory system performs an analysis of the operational data to assess performance of the decision-making logic, using pre-operational data generated prior to field operation of the controller and/or a performance assessment logic generated prior to field operation of the controller. The supervisory system generates an analysis output based on a result of the analysis.

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Baden, Switzerland

Yaman Cansin Evrenosoglu