The vibration sensor employs artificial intelligence/machine learning (AI/ML) techniques to intelligently monitor equipment status and identify and signal when different fault modes occur. The aiSensing solution is based on QuickLogic’s QuickAI platform including the ultra-low-power EOS S3 multi-core sensor processing SoC, QuickFeather development kit, and SensiML Analytics Toolkit for endpoint AI applications.

“With QuickLogic’s multi-core ultra-low power EOS S3 SoC plus Open Source QuickFeather Dev Kit and SensiML’s Analytics Toolkit, aiSensing has developed three generations of our AI Vibration Detector in less than six months to support different customer requirements,” says Dennis Chu, aiSensing’s chief technology officer. “Our resulting endpoint AI-based IoT solution helps us enable predictive maintenance applications with better performance and cost than cloud-based AI solutions, and positions us well for future growth.”

The aiSensing Predictive Maintenance (PdM) solution integrates AI/ML technology to monitor the status of manufacturing equipment locally without the need for an internet-based cloud connection. This approach, says the company, results in a robust, high performance, real-time, and high security predictive maintenance solution for end customers.

The total solution is also extremely low power and low cost, making it practical to implement for a wide range of manufacturing applications. In addition, the AI models used by the sensor can be easily and quickly customized for each piece of manufacturing equipment to achieve a high degree of accuracy.

The aiSensing smart vibration sensor is available now in industrial temperature grade (-40 to +85 degrees C). It is waterproof, dustproof, and explosion resistant.


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