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Toward improved production monitoring for manufacturing

​List, a CEA Tech institute, has developed a method that makes production monitoring indicator calculations more reliable. The novel and practical method, developed for a food production plant, is also compatible with the needs of other industries.

Published on 24 August 2017

The calculations used for the ongoing monitoring of production processes in industry depend on sensors on the production line. If a sensor is faulty, the calculations will be inaccurate. The extrapolation techniques generally used to fill in holes in the data are not always effective.

Researchers at List developed a more sophisticated prediction algorithm to make production monitoring more reliable. The algorithm uses a statistical regression analysis that takes into account the sensor's history and measurement redundancy. The algorithm then determines the best prediction strategy and creates a model. To confirm the method's effectiveness, actual data and data obtained through different algorithms were compared. "When there are not a lot of holes in the data, all of the methods perform similarly," said a List researcher. "However, when the amount of missing data is more substantial or if the process is very irregular, regression analysis is more effective."

The algorithm was validated on a wide range of data and is currently being scaled up for use in a food manufacturing plant. Its use will then be expanded to other industries. This very generic approach can be used for any continuous manufacturing process as well as for energy monitoring.

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