Sound is everywhere, even when you can’t hear it.

It is this noiseless sound, though, that says a lot about how machines function. 

Helsinki-based Noiseless Acoustics and Amsterdam-based OneWatt are relying on artificial intelligence (AI) to better understand the sound patterns of troubled machines. Through AI they are enabling faster and easier problem detection. Both companies were also finalists in last year’s New Energy Challenge, an initiative by Shell, YES!Delft, and Rockstart that looks at innovative technologies and solutions within European and Israeli startups for the energy transition. Following last year’s success, a 2018 edition of the challenge was just launched.

The value of noiseless sound

According to the U.S. Department of Energy, industrial motor use accounts for 25 percent of all electricity usage nationwide. Yet despite the vital roles they play, motors can fail for any number of reasons, leading to a loss in productivity and profitability.

But what if it was possible to transform that noiseless sound into value? Through the use of AI, sounds can be analyzed to detect machine failure. In other words: Making sound visible even when it can’t be heard. With the aid of non-invasive sensors, machine learning algorithms, and predictive maintenance solutions, failing components can be recognized at an early stage before they become a major issue.

OneWatt is preventing problems by listening to motors. Through its Embedded Acoustic Recognition Sensors (EARS) device, combined with machine learning and frequency analysis, OneWatt can detect and predict faults before they happen. This includes the what, when, and where of a problem.

16,000 sound clips of faulty motors

The startup used its device among the top eight motor faults in the industry. These ranged from bearing faults to soft footing faults. By doing so the company collected almost 2TB of acoustic data containing over 16,000 sound clips of faulty motors.

“Audio is the most apparent sign of mechanical failure,” Paolo Samontañez, CTO of OneWatt, told me in an interview. “Most of the faults are signaled in this domain because of the movement of the components in the motor creating friction. Visible light is not a good indicator since it is not able to see through the motor, and could not tell if the bearings are degrading.”

Ultrasound is an option to visualize the internals of the motor, but Samontañez says this is costly. It would also require an operator to move the transmitter and receiver around, similar to an ultrasound machine in a hospital. Audio is the ideal solution, mainly because it’s unobtrusive. This also happens to be a primary requirement when dealing with industrial facilities as they need assurance that there will be no negative effect on the motors when a device is installed.

Noiseless Acoustics is using a combination of hardware, software, and analytics when listening to sound.

With its NL Camera, the startup can locate problems using sound. Similar to thermal imaging, the NL Camera picks up heat images that signal noise on the screen. The information is then uploaded to the cloud where algorithms help to assess the issue.

NL Sense is another tool Noiseless Acoustics uses. It is a non-intrusive system that pinpoints the exact location where problems are. By using a compact wireless sensor hub and sensors, that can be placed on any given surface, it will automatically send the information to the cloud where it is analyzed and processed.

“Sound describes things, it’s completely its own world,” Kai Sakesla, CEO of Noiseless Acoustics, told me in an interview. He adds that once a sound signal has been isolated from a source AI is used to see if there is a problem.