IIoT Anomaly Detection Driven by STMicroelectronics Introduced by AWS and Klika Tech

By Chad Cox

Embedded Computing Design

April 06, 2021


Klika Tech and Amazon Web Services (AWS) released a development kit for deploying predictive maintenance in Industrial Internet of Things ecosystems (IIoT).

Built on AWS and featuring STMicroelectronics, the kit introduced a unique machine learning model for automated response to industrial equipment anomalies. 

The design increases the speed of equipment-level data processing and reduce sensor-to-cloud latency and bandwidth. It helps industrial operations eliminate unplanned downtime and more efficiently monitor operations and equipment maintenance.

The kit features data collection from sensors connected to STMicroelectronics' P-NUCLEO-WB55 multi-protocol, ultra-low-power devices for Bluetooth Low Energy (BLE) integration. That enables wireless transmission of data to STMicroelectronics' STM32MP1 Series Microprocessing Unit (MPU) and embedded evaluation boards running Amazon Greengrass Version 2. The MPU sends data to AWS IoT Core and a user dashboard, enabling sensor-to-cloud realtime monitoring of equipment performance, advanced system fault diagnosis and predictive analytics.

According to the company, operators can attach a range of sensors to industrial equipment such as:

  • Motors.
  • Gearboxes.
  • Fans.
  • Pumps.

The ​​​​​​​​​​​​​​sensors will measure actions such as vibration, volume, temperature, humidity and more. The kit provides operators with realtime assessment of thousands of Industrial equipment anomalies.

For more information, visit klika-tech.com

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