aiSensing Deploys Highly Successful End-Point AI Vibration Sensor Using SensiML Analytics Toolkit and QuickLogic EOS S3
SensiML Corporation, a leading developer of AI tools for building intelligent Internet of Things (IoT) endpoints, announced that its customer, aiSensing, has successfully completed and deployed an endpoint AI-based vibration sensor for a large multi-national manufacturer in Asia. This intelligent endpoint monitors vibration patterns for multiple machines, detects potential anomalies, and issues maintenance requests when necessary. The result is reduced equipment downtime and higher overall factory productivity. Since the AI implementation is local, rather than cloud-based, the system features low cost, low latency and fast reaction times while simultaneously providing higher data security.
aiSensing’s customer is one of the largest and most successful manufacturing companies in Taiwan. It is the leading manufacturer of specialty adhesives, footwear adhesives, hot-melt adhesives, and liquid and powder coatings. The company has adopted this Edge AI-based approach to detect anomalies for vacuum pumps and chilling machines used in its manufacturing flow, including problems related to lack of lubrication, water leakage, bearing failures, and belt failures. By identifying potential problems before they arise, maintenance issues can be addressed in a managed way rather than as ad hoc emergency situations. This type of predictive maintenance is a key component of modern smart manufacturing initiatives.
The AI-based endpoint was developed on a QuickLogic EOS S3 ultra-low power multi-core Arm Cortex MCU-based SoC, which delivered more than enough processing bandwidth for the application at a low cost. The AI application running on the QuickLogic device was built using the SensiML Analytics Toolkit, which provided a complete solution for the quick development of this sophisticated IoT endpoint.
“Smart manufacturing is a significant trend across a broad range of industries,” said Chris Rogers, chief executive officer at SensiML. “Predictive maintenance is one of the core initiatives in that trend, and aiSensing’s vibration sensor is a great example of how to effectively use AI to implement a practical and cost-effective predictive maintenance solution.”
“Our endpoint AI-based vibration sensor has been very successful,” said Dennis Chu, chief technology officer at aiSensing. “Its low power consumption, fast response times, and low cost are the ideal combination of features for this predictive maintenance application. With the SensiML tools, we can easily modify the design to address new and unique requirements for our customers.”
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