SensiML announced that its customer, aiSensing, successfully deployed an endpoint AI-based vibration sensor for a large multinational manufacturer in Asia. Developed using the SensiML Analytics Toolkit, the intelligent endpoint monitors vibration patterns for multiple machines, detects potential anomalies, and issues maintenance requests when necessary.
The system not only reduced equipment downtime, but also increased overall factory productivity. Since the AI implementation is local, rather than cloud-based, an endpoint system such as the one created by aiSensing affords low latency, fast reaction times, and minimized cost, while also providing higher data security. This type of predictive maintenance is a key component of modern smart manufacturing initiatives.
aiSensing’s vibration detection system leverages the QuickLogic EOS S3, a low-power multicore Arm Cortex MCU-based SoC with more than enough processing bandwidth for the application. The AI application running on the QuickLogic chip was constructed using the SensiML Analytics Toolkit.
The Analytics Toolkit enables ultra-low-power IoT endpoints that implement AI to transform raw sensor data into meaningful insight at the device itself. The development platform covers data collection, labeling, algorithm and firmware generation, and testing. It supports Arm Cortex-M class and higher microcontroller cores, Intel x86 instruction set processors, and QuickLogic SoCs and Quick AI platforms with FPGA optimization.
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