Article: Intelligent voiceprint monitoring system for wind turbine blades using SVDD and LSTM Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.96 pp.1 - 19 Abstract: The long-term safety, operational stability and economic efficiency of grid-connected wind power generation systems are of great significance to the accurate and continuous monitoring of wind turbine blade operating conditions. This paper proposed a new wind turbine blade acoustic monitoring scheme based on adaptive noise reduction, ulti-domain feature enhancement, and a two-stage fault detection method for non-contact blade condition assessment. The system directly addresses the critical technical gap of insufficient early fault capture capability and poor anti-interference performance of existing monitoring methods in complex and dynamic wind farm operating environments. Rigorous experimental validation shows that the system achieves 99.2% fault recognition accuracy, 47.6% improvement in anti-noise performance, 0.8-second early fault detection delay, and maintains a low false alarm rate of 1.2% under varying field operating conditions. Inderscience Publishers - linking academia, business and industry through research

Title: Intelligent voiceprint monitoring system for wind turbine blades using SVDD and LSTM

Authors: Jiandong Wei; Xianhao Fan

Addresses: State Energy Tengxian Energy Development Co., Ltd., Nanning, 530004, China ' College of Electrical Engineering, Guangxi University, Nanning, 530004, China

Abstract: The long-term safety, operational stability and economic efficiency of grid-connected wind power generation systems are of great significance to the accurate and continuous monitoring of wind turbine blade operating conditions. This paper proposed a new wind turbine blade acoustic monitoring scheme based on adaptive noise reduction, ulti-domain feature enhancement, and a two-stage fault detection method for non-contact blade condition assessment. The system directly addresses the critical technical gap of insufficient early fault capture capability and poor anti-interference performance of existing monitoring methods in complex and dynamic wind farm operating environments. Rigorous experimental validation shows that the system achieves 99.2% fault recognition accuracy, 47.6% improvement in anti-noise performance, 0.8-second early fault detection delay, and maintains a low false alarm rate of 1.2% under varying field operating conditions.

Keywords: wind turbine blade; acoustic monitoring; fault detection; signal processing.

DOI: 10.1504/IJICT.2026.156191

International Journal of Information and Communication Technology, 2026 Vol.27 No.96, pp.1 - 19

Received: 07 May 2026
Accepted: 13 Jun 2026
Published online: 07 Sep 2026 *

Title: Intelligent voiceprint monitoring system for wind turbine blades using SVDD and LSTM

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