Anomaly Detection in Binary Time Series Data: An unsupervised Machine Learning Approach for Condition Monitoring

Gábor Princz; Masoud Shaloo; Selim Erol

doi:10.1016/j.procs.2024.01.105

Anomaly Detection in Binary Time Series Data: An unsupervised Machine Learning Approach for Condition Monitoring

Gábor Princz
, Masoud Shaloo
, Selim Erol

Institute of Industrial Engineering and Management

University of Applied Sciences Wiener Neustadt

Research output: Contribution to journal › Conference article › peer-review

Original language	English
Pages (from-to)	1065-1078
Number of pages	14
Journal	Procedia Computer Science
Volume	232
DOIs	https://doi.org/10.1016/j.procs.2024.01.105
Publication status	Published - 1 Jan 2024
Event	5th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2023 - Lisbon, Portugal Duration: 22 Nov 2023 → 24 Nov 2023

Keywords

Anomaly Detection
Binary Time Series Data
Data-driven Maintenance
Smart Manufacturing
Unsupervised Machine Learning

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10.1016/j.procs.2024.01.105

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title = "Anomaly Detection in Binary Time Series Data: An unsupervised Machine Learning Approach for Condition Monitoring",

keywords = "Anomaly Detection, Binary Time Series Data, Data-driven Maintenance, Smart Manufacturing, Unsupervised Machine Learning",

author = "G{\'a}bor Princz and Masoud Shaloo and Selim Erol",

note = "Publisher Copyright: {\textcopyright} 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0); 5th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2023 ; Conference date: 22-11-2023 Through 24-11-2023",

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doi = "10.1016/j.procs.2024.01.105",

language = "English",

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journal = "Procedia Computer Science",

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TY - JOUR

T1 - Anomaly Detection in Binary Time Series Data

T2 - 5th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2023

AU - Princz, Gábor

AU - Shaloo, Masoud

AU - Erol, Selim

PY - 2024/1/1

Y1 - 2024/1/1

KW - Anomaly Detection

KW - Binary Time Series Data

KW - Data-driven Maintenance

KW - Smart Manufacturing

KW - Unsupervised Machine Learning

UR - https://www.scopus.com/pages/publications/85189773729

U2 - 10.1016/j.procs.2024.01.105

DO - 10.1016/j.procs.2024.01.105

M3 - Conference article

AN - SCOPUS:85189773729

SN - 1877-0509

VL - 232

SP - 1065

EP - 1078

JO - Procedia Computer Science

JF - Procedia Computer Science

Y2 - 22 November 2023 through 24 November 2023

ER -

Anomaly Detection in Binary Time Series Data: An unsupervised Machine Learning Approach for Condition Monitoring

Keywords

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