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Electrical arc faults pose serious safety risks and can cause costly system downtime. Our arc fault detection solution uses Machine Learning (ML) at the edge to detect hazardous arcs with exceptional speed and accuracy, outperforming traditional rule-based methods.
Designed for both Alternating Current (AC) and Direct Current (DC) systems, this solution delivers sub-millisecond detection while maintaining a minimal memory footprint.
Faster and more accurate fault detection
Minimize nuisance trips and downtime
Optimized models fit easily into embedded systems
Tested and validated with industry datasets and standards
Our arc fault detection solution is delivered as a turnkey firmware solution that runs entirely on a low-power microcontroller (MCU), eliminating the need to build or train models from scratch. Well-defined interfaces and tooling simplify integration into existing applications, enabling faster evaluation and deployment.
The arc fault detection model is optimized using curated electrical signal datasets and tuning techniques designed for safety-critical, real-time embedded systems. This proprietary approach enables highly accurate detection, minimal latency, efficient resource utilization and predictable behavior across supported platforms.
Sensor systems can detect aging and environment-driven degradation, predict and prevent system failures and provide early warning for hazardous leaks to ensure optimal safety and efficiency.
Our edge AI portfolio extends beyond audio to include intelligent facial recognition interfaces, predictive maintenance, and safety-critical monitoring. These solutions are optimized for real-world deployment, delivering secure, low-power inference at the edge without reliance on the cloud.
Our low-power audio interface solution enables always-on wake word detection and command recognition, delivering fast response times without streaming audio to the cloud.
Our facial recognition with liveness detection solution enables accurate, secure and fully on-device facial recognition, eliminating reliance on the cloud.
Our predictive maintenance solution analyzes sensor data such as vibration, current or temperature to detect anomalies early, helping prevent equipment failures and reduce unplanned downtime.
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