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Comment: ICT solutions enable the work in IMPROVE. Big Data is collected from the production plants and modern machine learning techniques are used to learn models from data, drastically reducing the manual modelling efforts. Learned models can adapt to the modern, fast changing systems and are available for tasks like condition monitoring and root cause analysis. The results are then displayed to the user, hiding the systems complexity.
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Comment: A modern production plant consists of many smaller systems which are connected to each other. Time synchronisation between the systems is often a problem whcih needs to be addressed for tasks like data acquisition.
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Comment: Data from complex production plants is acquired and used to learn models of machines and production processes which are then used for simulation, optimization and diagnosis tasks.
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Comment: Models are learned from process data and extended with expert knowledge which are then used to detect wear, predict maintenance windows and find root causes for errors during production.
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Comment: Modern communication technologies like OPC-UA are used to acquire data from the controls. Data from distributed controls must be synchronized for complex plant models. Developed model learning strategies can be implemented within the controls themselves when the circumstances allow this.
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Comment: The optimization can optimize different parameters/aspects of the plant, including but not limited to energy consumptions.
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Comment: Normal behaviour models of the plants are learned durign operation. These models can then be used to monitor the condition of the machine. New situations not covered by the originally learned model can be added through adaptive learning.
Optimization algorithms can find sub-optimal configurations of the plant and improve them. Newly calculated system parameters are verified in a simulation before they are applied to the real plant.
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Comment:
Optimization algorithms can calculate better plant configurations. Thesting these new configurations in a real plant can be very cost intensive as production may be compromised during configuration and testing. A virtual environment which is able to simulate new parameters and verify them is a big deal as the running production is not compromised during testing and evaluation of new parameters.
The main focus of IMPROVE are learned models. Manual modeling of system models is not suitable for the complex, fast chaning industrial plants we have today. Lots of expert knowlege is needed to manually create a model. Learned models can be created using only data and little to no expert knowledge is required depending on the technology.
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