Imagine if your machines could tell you they were getting sick before they actually broke down. This technology acts like a digital ear that listens to the sounds and vibrations of equipment to spot tiny changes. It learns what 'healthy' sounds like and alerts you the moment something sounds wrong, similar to how a mechanic can tell a car engine has a problem just by listening to it.
Industrial machines often fail unexpectedly, leading to expensive downtime and inefficient maintenance. Traditional monitoring methods often miss early warning signs of mechanical failure.
The nGuard platform, consisting of edge hardware, cloud software, a fleet dashboard, and an auto-calibration AI system for anomaly detection.
If you are an OEM dealing with unexpected equipment failure at client sites — this project developed an AI-based acoustic diagnostic tool that detects faults with 99.6% accuracy. This allows for early detection of broken machines, preventing costly downtime.
If you are a grid operator dealing with the instability of renewable energy sources — this project developed a multisensory monitoring approach that addresses challenges brought by electrification. It provides real-time overviews of machine fleets to ensure grid stability.
If you are a maintenance provider dealing with unnecessary manual inspections — this project developed the nGuard platform that automatically detects machine faults. This reduces the need for manual checks and optimizes maintenance schedules.
Based on available project data, specific pricing or cost structures are not disclosed, though the project received a EUR 2,500,000 EU contribution for development.
Yes, the nGuard platform includes a fleet dashboard for real-time and periodic overviews of machine fleets and manages a database exceeding one billion recordings.
Based on available project data, the company utilizes a proprietary audio dataset containing terabytes of data to power its algorithms, but specific licensing terms are not listed.
The system uses integrated hardware with embedded edge software and a cloud-based software platform for easy deployment and monitoring.
The project has developed a Novelty Data Detection module and semi-automated procedures to reduce the number of samples needed, allowing for quicker creation of custom AI models.
The project is led by a single Czech SME, NEURONSW SE. This 100% industry-led structure indicates a strong commercial drive, as the company has already scaled its technical and sales teams to move from research into active market penetration with OEMs.
Contact NEURONSW SE in the Czech Republic
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