A robust fleet-based anomaly detection framework applied to wind turbine vibration data-Reference-Cited by-同舟云学术

A robust fleet-based anomaly detection framework applied to wind turbine vibration data

Published:2023-11 Issue: Volume:126 Page:106859
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

de Novaes Pires Leite Gustavo^ORCID,Farias Felipe Costa,de Sá Tiago Gomes,Araújo da Costa Alexandre Carlos,Petribú Brennand Leonardo José,de Souza Marrison Gabriel Guedes,Villa Alvaro Antonio Ochoa^ORCID,Droguett Enrique Lopez^ORCID

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference66 articles.

1. An ensemble angle-based outlier detection for big data;Al-taei;Commun. Comput. Inf. Sci.,2019

2. A review of local outlier factor algorithms for outlier detection in big data streams;Alghushairy;Big Data Cogn. Comput.,2020

3. PyCaret: An open source, low-code machine learning library in Python;Ali,2020

4. Wind turbine reliability: A comprehensive review towards effective condition monitoring development;Artigao;Appl. Energy,2018

5. Wind turbine reliability: A comprehensive review towards effective condition monitoring development;Artigao;Appl. Energy,2018

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Fault detection framework in wind turbine pitch systems using machine learning: Development, validation, and results;Engineering Applications of Artificial Intelligence;2024-12

2. A Novel Proposal in Wind Turbine Blade Failure Detection: An Integrated Approach to Energy Efficiency and Sustainability;Applied Sciences;2024-09-09

3. Ensemble learning framework for fleet-based anomaly detection using wind turbine drivetrain components vibration data.;Engineering Applications of Artificial Intelligence;2024-07

4. Deep Multiscale Soft-Threshold Support Vector Data Description for Enhanced Heavy-Duty Gas Turbine Generator Sets’ Anomaly Detection;Shock and Vibration;2024-04-29