The research activities in the field Industrial Sound Analysis are focused on the development of AI-based methods for automated acoustic monitoring of products and processes for use in in-line and end-of-line quality control, process monitoring and predictive maintenance.
Main use cases for acoustic monitoring:
Acoustic monitoring can be used in machining or welding, among other applications. Use cases are: Inline monitoring, process monitoring or predictive maintenance.
Your car is a good example to show the potential of quality assessment based on acoustic signals. You know the familiar driving sound caused by the engine, tyre wear and airflow. But what do you feel when this noise changes? A rattling or grinding noise might worry you and make you wonder if your car is still safe to drive. You probably react to this unknown and unexpected condition by driving to the workshop or at least stopping your car. It's no different in industry. We analyse the sound of your product or production process and let you know when something is not working as expected.
In industry, wherever there is movement, there are audible sounds that indicate the quality of products or processes. At the Fraunhofer IDMT in Ilmenau, Germany, we develop AI-based methods for sound analysis - especially for the analysis of industrial sounds - and thus create innovative approaches for automated acoustic monitoring (amo) of products and processes. amo can be used along the entire value chain for quality assessment and offers added value where, for example, optical monitoring methods reach their limits.
Our team of scientists from the fields of data science, data analysis, software development and project management is therefore researching AI-based solutions for audio signal analysis. The innovation at amo is that the measurement data is processed without any connection to an external cloud, so our solutions can be used locally within the company or directly on the machine.
Machine and plant manufacturers are faced with the challenge of providing their customers with comprehensive sensor-based machine monitoring. However, existing methods cannot solve all the problems that arise, such as unexpected downtime, continuous monitoring of the machine or evaluation of existing machine data. In addition, there is a shortage of specialists who know their machines so well that they can immediately identify faults by sound and take action if necessary.
Reference project with Procter & Gamble
We have developed AI algorithms to analyze the sound of home appliances for monitoring their condition with over 97% accuracy. This enables the use of interactive apps to track the status of household activities and the involved device leading to improved user guidance and higher customer satisfaction.
We support the implementation and deployment of AI-powered acoustic quality control systems in manufacturing processes, from project definition to operational use.
Our scientists exchange information within their research community and with stakeholders from industry and present current research results at scientific meetings and conferences. In addition, we regularly publish articles in professional journals that deal with application topics in our research field, including the German speaking magazines "Maschinenmarkt" or "Fertigungstechnik".
Fraunhofer Institute for Digital Media Technology IDMT