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An Adaptable and Unsupervised TinyML Anomaly Detection System for Extreme Industrial Environments.

Mattia Antonini¹, Miguel Pincheira¹, Massimo Vecchio¹

¹Fondazione Bruno Kessler, Via Sommarive 18, 38123 Trento, Italy.

Sensors (Basel, Switzerland)

|February 28, 2023

Summary

This summary is machine-generated.

This study introduces an edge computing system for industrial anomaly detection using Tiny Machine Learning (TinyML) on IoT devices. It enables real-time failure prediction in harsh environments, enhancing operational reliability.

Keywords:

Internet of Things Tiny-MLOps TinyML anomaly detection blockchain machine learning

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Area of Science:

Engineering
Computer Science
Data Science

Background:

Industrial assets rely on sensors for status monitoring, with data analysis typically performed remotely via cloud or data centers.
Remote analysis presents challenges including system complexity, connectivity dependence, and unsuitability for extreme environments.
Existing methods are limited by communication restrictions and potential failures in isolated or harsh industrial settings.

Purpose of the Study:

To propose and evaluate an end-to-end adaptable anomaly detection system for extreme industrial environments.
To leverage Internet of Things (IoT), edge computing, and Tiny Machine Learning (TinyML) for real-time anomaly detection.
To address limitations of cloud-based analysis in terms of connectivity and operational constraints.

Main Methods:

Development of an IoT sensing Kit with an ESP32 microcontroller and MicroPython firmware for edge data processing.
Implementation of an anomaly detection model using the isolation forest algorithm trainable on the microcontroller.
Integration of blockchain technology for a secure and transparent anomaly record.

Main Results:

The isolation forest model trains on the microcontroller in 1.2 to 6.4 seconds.
Anomaly detection occurs in under 16 milliseconds using 50 trees and 80 KB of RAM.
The system successfully operates in an extreme industrial environment, demonstrating adaptability and configurability.

Conclusions:

The proposed edge computing system effectively performs real-time anomaly detection in extreme industrial settings.
TinyML and IoT integration on edge devices offer a robust and efficient alternative to cloud-based analysis.
The system enhances industrial asset monitoring by providing timely failure alerts and a secure anomaly log.