Fault Diagnosis Method for Wind Turbine Blades Through the Integration of Mel-Frequency Cepstral Coefficients and Intrinsic Energy Ratio
This paper presents a fault diagnosis method based on Mel-frequency cepstral coefficients (MFCC) and intrinsic energy ratio (IER) to improve efficiency in feature extraction from audio signals for diagnosing faults in wind turbine blades. This methods begins by extracting MFCCs and IERs from audio s...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Control and Information Technology
2025-02-01
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| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2025.01.006 |
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| Summary: | This paper presents a fault diagnosis method based on Mel-frequency cepstral coefficients (MFCC) and intrinsic energy ratio (IER) to improve efficiency in feature extraction from audio signals for diagnosing faults in wind turbine blades. This methods begins by extracting MFCCs and IERs from audio signals collected from the blades. These fault features are subjected to dimensionality reduction through dynamic time warping, resulting in composite MFCCs. Subsequently, the composite MFCCs, derived from the extraction of acoustic impulses of the blades, are denoised. Faults in the wind turbine blades are then classified utilizing a support vector machine (SVM). Experimental results demonstrated the feasibility and effectiveness of the proposed fault diagnosis method, with a fault recognition accuracy of up to 97.06%, which is of important significance for ensuring the stable operation and maintenance of wind turbines. |
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| ISSN: | 2096-5427 |