Research on the Construction of Fault Knowledge Graph for Wind Power Hydraulic Equipment DOI

Anyan Du,

Xiaoyu Ye,

Xin Ma

et al.

Journal of engineering system., Journal Year: 2024, Volume and Issue: 2(4), P. 38 - 45

Published: Dec. 1, 2024

Hydraulic systems are widely used in wind turbines. However, with the extension of operation time, failure rate hydraulic system gradually increases, which seriously drags down efficiency turbine. Therefore, it is urgent to find a fast and accurate fault identification method identify abnormality turbine system. The analysis documents collected over past 15 years constitute data set that has been processed by BIO annotation technique make suitable for analysis. Based on classic Bert-BILSTM-CRF entity recognition model, an optimized version developed. Firstly, Bert model collect extract features scattered light; then, these fused output vector BILSTM model; finally, CRF complete classification labels. By embedding strategy adversarial learning BERT architecture, robustness successfully enhanced. Subsequently, we will analyze overall architecture obtained triad information save Neo4j graph database promote its adaptability. Finally, help Python, have created knowledge mapping. study ultimately revealed achieved superior performance 93 F1 score. Up 1.8 percentage points. proposed exhibits enhancement roughly 56 points model.

Language: Английский

Adaptive signal regime for identifying transient shifts: A novel approach toward fault diagnosis in wind turbine systems DOI
Peng Chen, Y. Wu, Shuai Fan

et al.

Ocean Engineering, Journal Year: 2025, Volume and Issue: 325, P. 120798 - 120798

Published: March 6, 2025

Language: Английский

Citations

1

Predictive digital twin for wind energy systems: a literature review DOI Creative Commons

Ege Kandemir,

Agus Hasan, Trond Kvamsdal

et al.

Energy Informatics, Journal Year: 2024, Volume and Issue: 7(1)

Published: Aug. 8, 2024

Abstract In recent years, there has been growing interest in digital twin technology both industry and academia. This versatile found applications across various industries. Wind energy systems are particularly suitable for platforms due to the integration of multiple subsystems. study aims explore current state predictive wind by surveying literature from past five identifying challenges limitations, addressing future research opportunities. review is structured around four main questions. It examines commonly employed methodologies, including physics-based modeling, data-driven approaches, hybrid modeling. Additionally, it explores data sources such as IoT sensors, historical databases, external application programming interfaces. The also delves into key features technologies behind real-time systems, communication networks, edge computing, cloud computing. Finally, addresses platforms. Addressing these questions enables development modeling strategies with fusion algorithms, which allow interpretable real time. Filter methods dimensionality reduction algorithms minimize computational resource demand operating algorithms. Moreover, advancements high-bandwidth networks facilitate efficient transmission between physical assets twins reduced latency.

Language: Английский

Citations

3

An Explainable AI approach for detecting failures in air pressure systems DOI
Shawqi Mohammed Farea, Mehmet Emin Mumcuoğlu, Mustafa Ünel

et al.

Engineering Failure Analysis, Journal Year: 2025, Volume and Issue: unknown, P. 109441 - 109441

Published: Feb. 1, 2025

Citations

0

Quantum machine learning based wind turbine condition monitoring: State of the art and future prospects DOI Creative Commons

Zhefeng Zhang,

Yueqi Wu, Xiandong Ma

et al.

Energy Conversion and Management, Journal Year: 2025, Volume and Issue: 332, P. 119694 - 119694

Published: March 15, 2025

Language: Английский

Citations

0

Prediction of statistical force–displacement curves of Charpy-V impact tests based on unsupervised fracture surface machine learning DOI

Jack Rosenberger,

Johannes Tlatlik,

Nils Rump

et al.

Engineering Failure Analysis, Journal Year: 2025, Volume and Issue: unknown, P. 109551 - 109551

Published: March 1, 2025

Language: Английский

Citations

0

Design of a novel noise resilient algorithm for fault detection in wind turbines on supervisory control and data acquisition system DOI Creative Commons
Muhammad Irfan, Nabeel Ahmed Khan, Muhammad Abubakar

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 17, 2025

Language: Английский

Citations

0

Fault Warning Study of Gearbox Based on SOM-ASTGCN-BiLSTM and Mutual Diagnosis of Same Clustered Wind Turbines DOI
Bo Gu, Hongtao Zhang, Shuai Yue

et al.

Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 123442 - 123442

Published: May 1, 2025

Language: Английский

Citations

0

Exploring Multilayer Neural Networks to Predict Possible Failures of a Cobot’s Teach Pendant DOI

Héctor Rafael Morano Okuno,

Guillermo Sandoval Benitez

Published: Jan. 1, 2025

Language: Английский

Citations

0

Detecting APS failures using LSTM-AE and anomaly transformer enhanced with human expert analysis DOI
Mehmet Emin Mumcuoğlu, Shawqi Mohammed Farea, Mustafa Ünel

et al.

Engineering Failure Analysis, Journal Year: 2024, Volume and Issue: 165, P. 108811 - 108811

Published: Aug. 24, 2024

Language: Английский

Citations

2

Fault diagnosis for wind turbine generators based on Model-Agnostic Meta-Learning: A few-shot learning method DOI
Likui Qiao, Yuxian Zhang, Qisen Wang

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: unknown, P. 126171 - 126171

Published: Dec. 1, 2024

Language: Английский

Citations

2