The experienced machine operator or production engineer often sees and hears whether a production process is running properly or whether a machine is working flawlessly. He is therefore an important part of quality assurance. With sensors and corresponding signal analysis, automated test methods are used to try to emulate these human abilities. In contrast to visual AI-based methods, the potential of acoustic quality assurance in combination with machine learning methods is not yet exploited. In order to increase this potential, we face various challenges that arise in the industrial environment:
- Adaptive or easily adaptable test methods for flexible production with changing production and process parameters.
- High quality of automated production leads to a low number of "not ok” parts. Accordingly, machine learning algorithms that can work with less or unbalanced data are necessary.
- Reliable and robust detection even under noisy environmental conditions.