TinyML Towards Industry 4.0: Resource-Efficient Process Monitoring of a Milling Machine

TL;DR

This paper demonstrates a resource-efficient TinyML system for industrial process monitoring, achieving high accuracy and low power consumption on microcontrollers, thus enabling smart factory applications.

Contribution

It presents a complete TinyML pipeline for industrial machine monitoring, including dataset creation, model development, and deployment on microcontrollers, with a novel dataset and optimized CNN model.

Findings

Achieved 100% test accuracy with a quantized CNN

Model size of 12.59kiB enables deployment on microcontrollers

Inference time of 15.4ms with low energy consumption

Abstract

In the context of industry 4.0, long-serving industrial machines can be retrofitted with process monitoring capabilities for future use in a smart factory. One possible approach is the deployment of wireless monitoring systems, which can benefit substantially from the TinyML paradigm. This work presents a complete TinyML flow from dataset generation, to machine learning model development, up to implementation and evaluation of a full preprocessing and classification pipeline on a microcontroller. After a short review on TinyML in industrial process monitoring, the creation of the novel MillingVibes dataset is described. The feasibility of a TinyML system for structure-integrated process quality monitoring could be shown by the development of an 8-bit-quantized convolutional neural network (CNN) model with 12.59kiB parameter storage. A test accuracy of 100.0% could be reached at 15.4ms inference time and 1.462mJ per quantized CNN inference on an ARM Cortex M4F microcontroller, serving as a reference for future TinyML process monitoring solutions.