Industrial Sound Analysis

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.

• Condition monitoring of operational states of household devices
• >97% detection accuracy in any environment
• Robust to different device types
• Integrated in iOS and Android apps

Compressed air is an indispensable resource for many German industrial and trade companies for the operation of machines and plants. At the same time, it also represents a high cost factor on the electricity bill. On average, companies waste 30 percent of the energy generated due to the unnoticed escape of expensive compressed air. To detect such leaks, they rely, among other things, on ultrasound testing methods and on the mere hearing of trained personnel.

Fraunhofer IDMT has now tested in an experiment whether this "hearing" can be automated and reproduced using microphones in combination with machine learning methods in order to develop a reliable system for leakage detection. First results show that this is generally possible.

In this experiment, three different operating states were simulated: running, broken, and running under heavy load. Each motor is in one of these operational states which sound differently. The researchers placed a MEMS (micro-electro-mechanical systems for signal recording) microphone in front of the engines, which recorded the operating sounds of the running engines. Recordings were made for each engine, which were analyzed using machine learning techniques with and without background noise.

Using an Air-Hockey table modified for research purposes, the researchers are working on novel approaches for acoustic quality assurance in an industrial context. They use pucks, which are made of different materials and cause different "pling" sounds as soon as they hit the board of the game table. During the game, these acoustic signals occur so frequently and irregularly that they can be used for analysis by means of machine learning methods to make a reliable statement about the material the pucks are made of.

This non-contact method, in which Fraunhofer IDMT combines its many years of expertise in the fields of acoustic measurement technology, signal processing and machine learning, can be used, for example, to detect material defects or in the in-line monitoring of welding processes. If acoustically perceptible defects are already detected during the process, it can be aborted and restarted promptly. Fraunhofer IDMT's acoustic testing method is also non-destructive and thus serves to reduce expensive test scrap.

Three differently coated balls roll in random order through the ball track system. The moving noises of the balls are picked up by small microphones and analyzed by machine learning algorithms. In this way, the balls are monitored in real time and identified by their specific rolling sound. The analysis result - the order of arrival - is immediately shown graphically on a display. With this principle demonstrator, the institute shows new acoustic methods for quality assurance - contactless, non-destructive and safely integrated.