Condition Monitoring & Predictive Maintenance

Build condition monitoring and predictive maintenance
into components and equipment, running in real-time,
at the edge, on commodity MCUs.

Watch our Industrial Customer Use Case Video:

Reality AI and Siemens joint demonstration of the Ham-Let smart industrial valve 

At Hannover Messe 2018, the world’s most important industrial tradeshow, Reality AI and Siemens, along with Ham-Let Group, revealed a joint solution using sound to detect what is happening inside a valve.

In this video, Shay Benchorin from Siemens MindSphere and Agmon Porat from Ham-Let explain how they used Reality AI Tools™ to train classifiers from acoustic data, and deployed them at the gateway level for real-time condition monitoring of the valve. 

Make your machine a "Smart Machine" able to identify anomalies and predict:

  • Bearing or component wear

  • Remaining Useful Life

  • Specific conditions or events

  • Mechanical issues

  • Anomalous behavior or activity

Using any signal input alone or in combination:






Proprietary Sensors



Enable predictive maintenance on every component of a vehicle using real-time, edge AI

Automotive Tier 1 and Tier 2 suppliers around the world are using Reality AI to create smart components that:

  • Monitor their own status

  • Predict their own maintenance needs

From tires to the drive train, to windows and doors -- every single component on the vehicle, front bumper to rear, will soon be smart, lowering cost of operation and increasing reliability.

automotive illustration by Reality AI

Learn more with our Technical Whitepaper

Explore the technical details behind the Reality AI approach to machine learning with signals

  • Why signals require a different approach than other machine learning problems

  • The importance of "features" to effective machine learning

  • Why the FFT probably isn't good enough, and what other options are better

  • The difference between Reality AI and Deep Learning