A new machine-learning technique can pinpoint potential power grid failures and cascading traffic bottlenecks, in real time.

A new machine-learning technique could pinpoint potential power grid failures or cascading traffic bottlenecks in real time.

Identifying a malfunction in the nation’s power grid can be like trying to find a needle in an enormous haystack. Hundreds of thousands of interrelated sensors spread across the U.S. capture data on electric current, voltage, and other critical information in real time, often taking multiple recordings per second.

Sounds provide important information about how well a machine is running. ETH researchers have now developed a new machine learning method that automatically detects whether a machine is “healthy” or requires maintenance.

Whether railway wheels or generators in a power plant, whether pumps or valves—they all make sounds. For trained ears, these noises even have a meaning: devices, machines, equipment or rolling stock sound differently when they are functioning properly compared to when they have a defect or fault.

The sounds they make, thus, give professionals useful clues as to whether a machine is in a good—or “healthy”—condition, or whether it will soon require maintenance or urgent repair. Those who recognize in time that a machine sounds faulty can, depending on the case, prevent a costly defect and intervene before it breaks down. Consequently, the monitoring and analysis of sounds have been gaining in importance in the operation and maintenance of technical infrastructure—especially since the recording of tones, noises and acoustic signals is made comparatively cost-effective with modern microphones.

Social media conglomerate Meta has announced a new research project to build a “universal speech translator” powered by artificial intelligence. Such a translation tool could be hugely beneficial and would help propel Meta’s platforms like Facebook and Instagram around the world.