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

Industrial assets often feature multiple sensing devices to keep track of their status by monitoring certain physical parameters. These readings can be analyzed with machine learning (ML) tools to identify potential failures through anomaly detection, allowing operators to take appropriate correctiv...

詳細記述

書誌詳細

出版年:	Sensors
主要な著者:	Mattia Antonini, Miguel Pincheira, Massimo Vecchio, Fabio Antonelli
フォーマット:	論文
言語:	英語
出版事項:	MDPI AG 2023-02-01
主題:	Internet of Things TinyML Tiny-MLOps machine learning anomaly detection blockchain
オンライン･アクセス:	https://www.mdpi.com/1424-8220/23/4/2344