Journal of
Systemics, Cybernetics and Informatics
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ISSN: 1690-4524 (Online)


Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.

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Published by
The International Institute of Informatics and Cybernetics


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Academia.edu
(A Community of about 40.000.000 Academics)


Honorary Editorial Advisory Board's Chair
William Lesso (1931-2015)

Editor-in-Chief
Nagib C. Callaos


Sponsored by
The International Institute of
Informatics and Systemics

www.iiis.org
 

Editorial Advisory Board

Quality Assurance

Editors

Journal's Reviewers
Call for Special Articles
 

Description and Aims

Submission of Articles

Areas and Subareas

Information to Contributors

Editorial Peer Review Methodology

Integrating Reviewing Processes


Transdisciplinary Communication as a Meta-Framework of Digital Education
Rusudan Makhachashvili, Ivan Semenist
(pages: 1-6)

Multidisciplinary Learning Using Online Networking in Biomedical Engineering
Shigehiro Hashimoto
(pages: 7-12)

Augmented Intelligence for Advancing Healthcare
Mohammad Ilyas
(pages: 13-19)

A Transdisciplinary Approach to Refereeal
Russell Jay Hendel
(pages: 20-25)

The Impact of Convictions on Interlocking Systems
Teresa Henkle Langness
(pages: 26-33)

Collaborative Convergence: Finding the Language for Trans-Disciplinary Communication to Occur
Cristo Leon, James Lipuma
(pages: 34-37)

Bridging the Gap Between the World of Education and the World of Business via Standards to Develop Competences of the Future at Universities
Paweł Poszytek
(pages: 38-42)

Multidisciplinary Learning for Multifaceted Thinking in Globalized Society
Shigehiro Hashimoto
(pages: 43-48)

From Spirituality to Technontology in Education
Florent Pasquier
(pages: 49-52)

Differentiated Learning and Digital Game Based Learning: The KIDEDU Project
Eleni Tsami
(pages: 53-57)

Emerging Role of Artificial Intelligence
Mohammad Ilyas
(pages: 58-65)

Practicing Transdisciplinarity and Trans-Domain Approaches in Education: Theory of and Communication in Values and Knowledge Education (VaKE)
Jean-Luc Patry
(pages: 66-71)

Reflexive Practice for Inter and Trans Disciplinary Research in the Third Millennium
Maria Grazia Albanesi
(pages: 72-76)


 

Abstracts

 


ABSTRACT


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

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


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 data is not commonly labeled by industry because of the manual and specialized effort that they require. In situations where labels are nonexistent or cannot be developed, unsupervised machine learning has been successfully applied for pattern recognition in large and multivariate datasets. Thus, instead of experts labeling a large amount of structured and/or non-structured data instances (also referred to as Big Data), unsupervised machine learning allows the annotation of the dataset from the few underlying interesting patterns detected. Therefore, we evaluate the performance of six unsupervised learning algorithms for the identification of anomalous patterns from a turbogenerator installed and operating in an oil and gas platform. The algorithms were C-AMDATS, Luminol Bitmap, SAX-REPEAT, k-NN, Bootstrap, and Robust Random Cut Forest. The evaluation performance was quantitatively calculated with seven classification metrics. The C-AMDATS algorithm was able to effectively and better detect the anomalous patterns, and it presented an accuracy of 99%, which leverages the further development of supervised DL models.

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