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feat(latex): add new major literature review

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Rifqi D. Panuluh
2025-10-13 08:41:49 +00:00
parent 3d9223a565
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latex/bibliography.bib
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@@ -190,6 +190,22 @@
file = {C\:\\Users\\damar\\Zotero\\storage\\59EASW6K\\Avci et al. - 2021 - A review of vibration-based damage detection in ci.pdf;C\:\\Users\\damar\\Zotero\\storage\\GQZUKPQN\\10.1016@j.ymssp.2020.107077.pdf.pdf}
}
@inproceedings{van2020,
title = {Statistical {{Feature Extraction}} in {{Machine Fault Detection}} Using {{Vibration Signal}}},
booktitle = {2020 {{International Conference}} on {{Information}} and {{Communication Technology Convergence}} ({{ICTC}})},
author = {Van, Bui and Van Hoa, Nguyen and Nguyen, Huy and Jang, Yeong Min},
date = {2020-10},
pages = {666--669},
issn = {2162-1233},
doi = {10.1109/ICTC49870.2020.9289285},
url = {https://ieeexplore.ieee.org/document/9289285/figures#figures},
urldate = {2024-08-26},
abstract = {Gearbox faults are one of the most common types in the industrial factory environment. Early detection of these faults allows fast replacement rather than a costly emergency. Nowadays, early machine fault detection application is improving due to the improvement of the IoT network and real-time analysis. The vibration signal is collected from Spectra Quest's Gearbox Prognostics Simulator and analyzed for fault classification. The preprocessing includes fast Fourier transform and statistical feature extraction. The AI algorithms are Artificial Neural Network, Logistic Regression, and Support Vector Machine. The highest accuracy reached is 100\%.},
eventtitle = {2020 {{International Conference}} on {{Information}} and {{Communication Technology Convergence}} ({{ICTC}})},
keywords = {ANN,Fault detection,Feature extraction,LR,Machine Learning,Manufacturing,Production facilities,Real-time systems,Support vector machines,SVM,Vibration Signal,Vibrations},
file = {C\:\\Users\\damar\\Zotero\\storage\\TW69QG8K\\van2020.pdf.pdf;C\:\\Users\\damar\\Zotero\\storage\\AZM769D7\\figures.html}
}
@article{katam2025,
title = {Machine Learning-Driven Structural Health Monitoring: {{STFT-based}} Feature Extraction for Damage Detection},
shorttitle = {Machine Learning-Driven Structural Health Monitoring},
@@ -811,22 +827,6 @@
file = {C\:\\Users\\damar\\Zotero\\storage\\6XITVIKY\\toh2020.pdf.pdf;C\:\\Users\\damar\\Zotero\\storage\\9L9KXB7V\\Toh and Park - 2020 - Review of Vibration-Based Structural Health Monito.pdf}
}
@inproceedings{van2020,
title = {Statistical {{Feature Extraction}} in {{Machine Fault Detection}} Using {{Vibration Signal}}},
booktitle = {2020 {{International Conference}} on {{Information}} and {{Communication Technology Convergence}} ({{ICTC}})},
author = {Van, Bui and Van Hoa, Nguyen and Nguyen, Huy and Jang, Yeong Min},
date = {2020-10},
pages = {666--669},
issn = {2162-1233},
doi = {10.1109/ICTC49870.2020.9289285},
url = {https://ieeexplore.ieee.org/document/9289285/figures#figures},
urldate = {2024-08-26},
abstract = {Gearbox faults are one of the most common types in the industrial factory environment. Early detection of these faults allows fast replacement rather than a costly emergency. Nowadays, early machine fault detection application is improving due to the improvement of the IoT network and real-time analysis. The vibration signal is collected from Spectra Quest's Gearbox Prognostics Simulator and analyzed for fault classification. The preprocessing includes fast Fourier transform and statistical feature extraction. The AI algorithms are Artificial Neural Network, Logistic Regression, and Support Vector Machine. The highest accuracy reached is 100\%.},
eventtitle = {2020 {{International Conference}} on {{Information}} and {{Communication Technology Convergence}} ({{ICTC}})},
keywords = {ANN,Fault detection,Feature extraction,LR,Machine Learning,Manufacturing,Production facilities,Real-time systems,Support vector machines,SVM,Vibration Signal,Vibrations},
file = {C\:\\Users\\damar\\Zotero\\storage\\TW69QG8K\\van2020.pdf.pdf;C\:\\Users\\damar\\Zotero\\storage\\AZM769D7\\figures.html}
}
@article{vos2022,
title = {Vibration-Based Anomaly Detection Using {{LSTM}}/{{SVM}} Approaches},
author = {Vos, Kilian and Peng, Zhongxiao and Jenkins, Christopher and Shahriar, Md Rifat and Borghesani, Pietro and Wang, Wenyi},