Ultra-low Power Embedded Unsupervised Learning Smart Sensor for Industrial Fault Detection - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

Ultra-low Power Embedded Unsupervised Learning Smart Sensor for Industrial Fault Detection

Résumé

In this paper, an ultra-low power embedded unsupervised learning smart vibration sensor is proposed for automatic industrial monitoring and fault detection. Using Kmeans algorithm, it is able to detect abnormal vibrations patterns. Architecture of the system is first presented, then embedded processing algorithms composed of feature extraction and kmeans algorithm are detailed, and finally an implementation on a vibrations simulator machine is described. Results show that faults can be detected with an accuracy of XX% using less than 0.15% of average embedded processor resources on a ARM M4F with an average consumption of 80µW. This smart sensor is relevant for Industrial Internet Of Things (IoT) autonomous monitoring applications, having more than one year of battery life using a single CR2032 coin cell.
Fichier principal
Vignette du fichier
2020 - CONF IEEE - Unsupervised Embedded Learning.pdf (4.14 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03610004 , version 1 (16-03-2022)

Identifiants

  • HAL Id : hal-03610004 , version 1

Citer

Sebastián Marzetti, Valentin Gies, Valentin Barchasz, Hervé Barthélemy, Hervé Glotin. Ultra-low Power Embedded Unsupervised Learning Smart Sensor for Industrial Fault Detection. 2020 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS), Jan 2021, Bali, Indonesia. ⟨hal-03610004⟩
58 Consultations
25 Téléchargements

Partager

Gmail Mastodon Facebook X LinkedIn More