tinyML Talks: Industry 4.0: Predictive Maintenance using Arduino Portenta H7 and Edge Impulse

Industry 4.0: Predictive Maintenance using Arduino Portenta H7 and Edge Impulse”

Manivannan Sivan
Lead engineer on the computer vision platform
Valeo

The proposed method explains the potential of TinyML in Industrial 4.0. This TinyML model uses Arduino Portenta H7 and Edge Impulse to predict the anomalous operation in Industrial machineries like Pump, valves & fans. For Industrial machineries audio datasets, the proposed method uses open source datasets – MIMII. This dataset contains an audio of malfunctioning industrial machines. It contains the sounds generated from four types of industrial machines, i.e. valves, pumps, fans, and slide rails. Each type of machine includes seven individual product models*1, and the data for each model contains normal sounds (from 5000 seconds to 10000 seconds) and anomalous sounds (about 1000 seconds).The model is trained using Edge Impulse with 1-D Convolution layer and followed by neural network layers.

source