FLAGS: A methodology for adaptive anomaly detection and root cause analysis on sensor data streams by fusing expert knowledge with machine learning

被引：0

作者：

Steenwinckel, Bram ^{[1
]}

De Paepe, Dieter ^{[1
]}

Vanden Hautte, Sander ^{[1
]}

Heyvaert, Pieter ^{[1
]}

Bentefrit, Mohamed ^{[2
]}

Moens, Pieter ^{[1
]}

Dimou, Anastasia ^{[1
]}

Van Den Bossche, Bruno ^{[2
]}

De Turck, Filip ^{[1
]}

Van Hoecke, Sofie ^{[1
]}

Ongenae, Femke ^{[1
]}

机构：

[1] Steenwinckel, Bram

[2] De Paepe, Dieter

[3] Vanden Hautte, Sander

[4] Heyvaert, Pieter

[5] Bentefrit, Mohamed

[6] Moens, Pieter

[7] Dimou, Anastasia

[8] Van Den Bossche, Bruno

[9] De Turck, Filip

[10] Van Hoecke, Sofie

[11] Ongenae, Femke

来源：

Steenwinckel, Bram (bram.steenwinckel@ugent.be) | 1600年 / Elsevier B.V., Netherlands卷 / 116期

关键词：

Fault detection - Semantic Web - Mining - Anomaly detection - Machine learning - Feedback;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

引用

页码：30 / 48

共 30 条

[1] FLAGS: A methodology for adaptive anomaly detection and root cause analysis on sensor data streams by fusing expert knowledge with machine learning
Steenwinckel, Bram
De Paepe, Dieter
Hautte, Sander Vanden
Heyvaert, Pieter
Bentefrit, Mohamed
Moens, Pieter
Dimou, Anastasia
Van Den Bossche, Bruno
De Turck, Filip
Van Hoecke, Sofie
Ongenae, Femke
[J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2021, 116 : 30 - 48
[2] Adaptive Anomaly Detection and Root Cause Analysis by Fusing Semantics and Machine Learning
Steenwinckel, Bram
[J]. SEMANTIC WEB: ESWC 2018 SATELLITE EVENTS, 2018, 11155 : 272 - 282
[3] Assembly Line Anomaly Detection and Root Cause Analysis Using Machine Learning
Abdelrahman, Osama
Keikhosrokiani, Pantea
[J]. IEEE ACCESS, 2020, 8 : 189661 - 189672
[4] A Tool to Combine Expert Knowledge and Machine Learning for Defect Detection and Root Cause Analysis in a Hot Strip Mill
Latham S.
Giannetti C.
[J]. SN Computer Science, 4 (5)
[5] Anomaly Detection and Root Cause Analysis on Log Data
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Shah, Ali Hussain
Zadeh, Esmaeil Habib
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[J]. ARTIFICIAL INTELLIGENCE XXXIX, AI 2022, 2022, 13652 : 333 - 339
[6] Methodology for Machine Learning Anomaly Detection in Phasor Measurement Unit Data
Baxter, Jacob
St Leger, Aaron
[J]. 2022 IEEE KANSAS POWER AND ENERGY CONFERENCE (KPEC 2022), 2022,
[7] Explainable Machine Learning in Industry 4.0: Evaluating Feature Importance in Anomaly Detection to Enable Root Cause Analysis
Carletti, Mattia
Masiero, Chiara
Beghi, Alessandro
Susto, Gian Antonio
[J]. 2019 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC), 2019, : 21 - 26
[8] Combining expert knowledge and unsupervised learning techniques for anomaly detection in aircraft flight data
Mack, Daniel L. C.
Biswas, Gautam
Khorasgani, Hamed
Mylaraswamy, Dinkar
Bharadwaj, Raj
[J]. AT-AUTOMATISIERUNGSTECHNIK, 2018, 66 (04) : 291 - 307
[9] Hybrid Machine Learning Model for Anomaly Detection in Unlabelled Data of Wireless Sensor Networks
Srivastava, Anushka
Bharti, Manoranjan Rai
[J]. WIRELESS PERSONAL COMMUNICATIONS, 2023, 129 (04) : 2693 - 2710
[10] Hybrid Machine Learning Model for Anomaly Detection in Unlabelled Data of Wireless Sensor Networks
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Manoranjan Rai Bharti
[J]. Wireless Personal Communications, 2023, 129 : 2693 - 2710

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