Unsupervised Machine Learning for Anomaly Detection in Multivariate Time Series Data of a Rotating Machine from an Oil and Gas Platform

Unsupervised Machine Learning for Anomaly Detection in Multivariate Time Series Data of a Rotating Machine from an Oil and Gas Platform

Deep Learning (DP) models have been successfully applied to detect and predict failures in rotating machines. However, these models are often based on the supervised learning paradigm and require annotated data with operational status labels (e.g. normal or failure). Furthermore, machine measurement...

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Bibliographic Details
Main Authors: Ilan Sousa Figueirêdo, Tássio Farias Carvalho, Wenisten Dantas da Silva, Lílian Lefol Nani Guarieiro, Alex Alisson Bandeira Santos, Leonildes Soares De Melo Filho, Ricardo Emmanuel Vaz Vargas, Erick Giovani Sperandio Nascimento
Format: Article
Language:English
Published: International Institute of Informatics and Cybernetics 2021-12-01
Series:Journal of Systemics, Cybernetics and Informatics
Subjects:
multivariate time series
rotating machinery
pattern recognition
anomaly detection
unsupervised machine learning
Online Access:http://www.iiisci.org/Journal/PDV/sci/pdfs/ZA422HO21.pdf

Internet

http://www.iiisci.org/Journal/PDV/sci/pdfs/ZA422HO21.pdf

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