Condition monitoring is a major component of the predictive maintenance systems, allowing production performance improvement, maintenance cost reduction and a drastic decrease of the downtime due to routine maintenance.
The FP-AI-NANOEDG1 function pack helps to jump-start the implementation and development of condition monitoring applications designed with the NanoEdge™ AI Studio (NanoEdgeAIStudio). It implements a full system, which can be deployed in the field for data collection and for validating accuracy in live conditions.
NanoEdge™ AI Studio (NanoEdgeAIStudio) simplifies the creation of autonomous Machine Learning libraries with the possibility of running not just inference but also training on the edge. It facilitates the integration of predictive maintenance capabilities as well as the security and detection with sensor patterns self-learning and self-understanding, exempting users from advanced skills in mathematics, Machine Learning, data science, or creation and training of Neural Network.
FP-AI-NANOEDG1 covers the entire design of the Machine Learning cycle from the data set acquisition to the integration of NanoEdge™ AI Studio generated libraries on a physical node.
It runs data collection, learning session and the inference in real time on an STM32L4R9ZI ultra-low-power microcontroller (Arm® Cortex®-M4 at 120 MHz with 2 Mbytes of Flash memory and 640 Kbytes of SRAM), taking physical sensor data as input. The SensorTile wireless industrial node (STEVAL-STWINKT1B) embeds industrial-grade sensors, including 6-axis IMU, 3-axis accelerometer and vibrometer to catch any movement precisely, a very high-frequency digital and analog microphone as well as ultrasound spectra enabling fine variation monitoring and also high-precision temperature, pressure and humidity sensor for challenging industrial environment.
The NanoEdge™ AI library generation itself is out of the scope of this function pack and must be generated using NanoEdge™ AI Studio (NanoEdgeAIStudio).
FP-AI-NANOEDG1 implements a wired interactive CLI to configure the node, record the data, and manage the learn and detect phases. For simple operation in the field, a standalone battery-operated mode allows basic controls through the user button, without using the console.