MERINO LASO, Pedro; BROSSET, David; PUENTES, John

doi:10.1109/SAI.2017.8252263

Métadonnées

Licence d’utilisation du document

MERINO LASO, Pedro
Lab-STICC_IMTA_CID_DECIDE
Département lmage et Traitement Information [IMT Atlantique - ITI]
Chaire cyberdéfense systèmes navals (Ecole Navale, IMT-Atlantique, THALES, DCNS)

BROSSET, David
Institut de Recherche de l'Ecole Navale [IRENAV]
Chaire cyberdéfense systèmes navals (Ecole Navale, IMT-Atlantique, THALES, DCNS)

PUENTES, John
Lab-STICC_IMTA_CID_DECIDE
Département lmage et Traitement Information [IMT Atlantique - ITI]
Chaire cyberdéfense systèmes navals (Ecole Navale, IMT-Atlantique, THALES, DCNS)

Langue

Communication dans un congrès avec actes

Ce document a été publié dans

Proceedings Computing 2017 : Science and Information Conference, Computing 2017 : Science and Information Conference, 2017-07-18, Londres. 2017p. 1330 - 1338

IEEE

Résumé en anglais

Sensor networks are becoming ubiquitous, enabling to improve decision-making and reducing human interaction by means of automatic or semi-automatic responses. However, due to deterioration or induced effects, sensors measures can be affected and produce anomalies that could alter decision-making. Most of the existing methods to identify sensors irregularities focus basically on detecting and discarding anomalous values, without looking for complementary information to understand generated anomalies. This paper presents an approach to obtain such complementary information by categorizing sensor anomalies, based on multidimensional quality assessment. It consists of two processing stages: an evaluation of data and information streams to estimate data quality imperfections and information quality dimensions; followed by the determination of agreement limits, compliant with normal states, to identify and categorize anomalies. The case study of discrete and analog sensors system installed in a simulator training platform of fuel tanks is presented, to illustrate an application of the proposed approach, considering 13 experimentally evaluated anomalies.< Réduire

Mots clés en anglais

Sensor

Anomaly categorization

Data quality

Information quality

Cyber-physical system

Métadonnées

Partager cette publication !

Licence d’utilisation du document

Analysis of Quality Measurements to Categorize Anomalies in Sensor Systems

Langue

Ce document a été publié dans

Résumé en anglais

Mots clés en anglais

URI

DOI

Origine

Unités de recherche