Today, a large variety of sensor information is already utilized for status monitoring of machines and systems in production. One particularly promising approach due to its low cost and flexibility is acoustic event recognition. The use of structure-borne sound (vibrations) has established itself in the area of machine condition monitoring, while airborne sound measurements for this purpose are barely known. The individual solutions already available on the market for this are very expensive due in particular to the microphones used. Moreover, these special applications with limited features cannot be easily applied to other tasks.
The project is based on the combination of new kinds of highly integrated sensor systems with innovative signal processing algorithms. This should support monitoring of not only the respective system components themselves but also their environment, which will increase the reliability of the entire system. The advantages of the solution lie in the fact that it operates energy independently to wirelessly receive and forward information.
The planned acoustic condition monitoring electronic platform (ACME) can also be quickly configured for use with other components via software. This platform makes it possible to quickly and conveniently design individual applications that incorporate intelligent, distributed, communicative and self-adapting processes.
In this project, the EAS division is focused primarily on the hardware platform concept and the development of algorithms. The work is directed toward the design of integrated sensor-reading circuits, including analog-digital conversion. The researchers are also applying their experience from the realization of condition monitoring systems on machines and systems and the associated signal analysis and data processing algorithms. The goal is to research new methods for event and condition detection and to adapt them to the requirements of the project.