Fault Identification Technology of Wind Turbine Blade Damage Sound Source Localization and Multimodal Data Fusion Based on Voiceprint Monitoring 核心 · 已核验

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nt. Finally, damage sound source localization and fault classification are achieved using the Time Difference of Arrival (TDOA) algorithm and Convolutional Neural Network (CNN). Experimental results show that the proposed method achieves an accuracy and recall rate of over 85% under various wind turbine fault conditions, with a localization error within 0.15m. Additionally, a real - time monitoring system based on an embedded hardware platform is designed. It can perform online fault identification via data stream processing and issue timely fault warnings to ensure the safe operation of wind turbines.As wind energy rapidly develops globally, wind turbine blades, as crucial components, require effective damage detection to ensure the reliability and longevity of wind turbines.

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落地页

https://figshare.com/articles/dataset/_b_Fault_Identification_Technology_of_Wind_Turbine_Blade_Damage_Sound_Source_Localization_and_Multimodal_Data_Fusion_Based_on_Voiceprint_Monitoring_b_/29400839

许可证

CC-BY-4.0 (置信:verified_official)

发布年份

2025

发布方

figshare

设备类型

wind_turbine

PHM 任务

fault_diagnosis fault_detection

分发点

故障工况