A number of fascinating projects have emerged that aim to use AI to better understand the way vibrations can give us insight into the health (or not) of machinery.
A recent paper from the University of Alabama in Huntsville describes a new algorithm that aims to provide a diagnostic function for the mechanical systems that form such a big part of our commercial engine.
Automated diagnostics
“The ability to extract dependable and actionable information from the vibration of machines will allow businesses to keep their assets running for longer while spending far less in maintenance. Also, the investment to get there will be just software,” the researcher says.
A number of companies are deploying this kind of approach in the real world, with German startup KONUX making headway in the railway industry.
An Israeli company, called 3DSignals, are doing a similar thing, utilizing deep learning to monitor sounds for deviations from the norm in mechanical acoustics. The company hope to place their ultrasonic technology inside cars, and especially into autonomous taxis. The ultimate goal is to detect problems before they become serious enough to take the vehicle off the road.
“When trained, the 3DSignals deep learning algorithms are able to identify [and] predict specific problems in advance with 98 percent accuracy,” the company said recently.
These kind of predictive maintenance technologies are already in place in many industries, and indeed back in 2014 an Accenture report predicted that investment in the Industrial Internet of Things would reach $500 billion by 2020.
A combination of cheap sensors, powerful data processing and machine learning has enabled companies to make their industrial processes significantly smarter and more efficient.
3DSignals clearly hope to make a similar dent in the automobile industry, and with most major manufacturers investing heavily in the future of motoring as a service, it’s a development that seems inevitable.
Check out the video below to see more about the company.
https://youtu.be/VgOEt_gcfOY
