Basic technologies for predictive maintenance are networked sensor nodes and a central data repository, so the topic is closely linked to the technology trends Internet of Things (IIoT), Industry 4.0. A model trained by machine learning with the centrally stored data can later also be used at the decentralized nodes for data evaluation.
In the KIWA project, a Fraunhofer EMFT research team combines sensor data from a manufacturing plant with other, external sensors to generate additional relevant characteristic values such as the vibration of a drive. Critical plant components are then monitored during operation and correlated to the process flow. An application example are plants of the project partner Mühlbauer: There, additional, external sensors such as vibration sensors or sensor systems for the exact measurement of further parameters are installed for an exact recording of the time sequences in order to predict maintenance or the replacement of drive systems and plant components of the production plant. The data collected is to be used to draw conclusions about the current operating condition and to make predictions about the further service life or necessary maintenance requirements.