Predictive maintenance with Arduino and Edge Impulse: Why and How to?
Channel: Hardware.ai
Date: 2021-05-28

We’re going to be using data from Wio Terminal built-in accelerometer, perform neural network model inference and anomaly detection on-device and then send data to new version of Blynk IoT platform with plethora of new features, which was launched these few days and fully supports Wio Terminal.

Link to the article:
https://www.hackster.io/dmitrywat/tinyml-course-5-anomaly-detection-for-predictive-maintenance-9efa11

Hardware.ai Twitter 
https://twitter.com/HardwareAi

Wio Terminal
https://www.seeedstudio.com/Wio-Terminal-p-4509.html

Seeed Arduino examples repository with code:
https://github.com/Seeed-Studio/Seeed_Arduino_Sketchbook/tree/master/examples/WioTerminal_Blynk_Edgent
https://github.com/Seeed-Studio/Seeed_Arduino_Sketchbook/tree/master/examples/WioTerminal_TinyML_5_Anomaly_Detection

Edge Impulse Public project:
https://studio.edgeimpulse.com/public/31205/latest

0:00 Intro
0:57 Anomaly detection and predictive maintenance
4:10 Data collection, processing and model training
9:10 Model testing with LCD screen output
12:29 Blynk integration
17:54 WiFi manager demonstration
19:13 Edge Impulse + Blynk application
20:05 Conclusion

Credits for the music:
Prism - A Chillwave Mix
https://youtu.be/nftWIonZy9E
Disc System - Ceti Alpha Six
Echosoft - Blue Eye Horizon