DRESDEN, Germany—Engineers at the Fraunhofer Institute for Photonic Microsystems (IPMS) here have developed a new system that combines sensor technology, data acquisition and AI-based data evaluation for condition monitoring and predictive maintenance. It opens up new possibilities for the preventive maintenance of plants and machines.

“Our [system] enables precise condition monitoring of machines through the use of sensors and intelligent data analysis,” says Marcel Jongmanns, Ph.D., project leader at Fraunhofer IPMS. “By integrating AI into the sensors, we can detect damage before it occurs, optimize maintenance intervals and minimize downtime.”

The system uses multimodal sensors that record accelerations in spatial directions and corresponding rotation rates. In addition, magnetic field sensors and acoustic or ultrasonic sensors are used to monitor industrial equipment.

According to Jongmanns, the system provides two main functions: Belt tension detection and jam detection. The AI models are based on extensive data analysis and enable accurate prediction of damage. To increase the accuracy of the models, real-time calibrations can be performed to adapt the system to new environments.

“Changing environmental influences can be directly modeled or taken into account in the analysis,” says Jongmanns. “This enables the integration of a large number of sensors and significantly increases the accuracy of predictions about the condition of the industrial equipment. Existing limitations in computing power for real-time modeling in embedded systems are overcome.”