Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (10): 3119-3130.doi: 10.13229/j.cnki.jdxbgxb.20240617

Previous Articles    

Fault detection and classification of wind turbine blades based on machine learning

Qiu-zhan ZHOU1(),Yan MU1,Hui-nan WU1,Xiao CHEN1,Feng WANG2,Chen LI2,Wen ZHANG2,Ping-ping LIU3,Cong WANG1()   

  1. 1.College of Communication Engineering，Jilin University，Changchun 130012，China
    2.Sinovel Wind Power Technology（jiangsu） Co. ，Ltd. ，Yancheng 224056，China
    3.College of Computer Science and Technology，Jilin University，Changchun 130012，China
  • Received:2024-06-03 Online:2025-10-01 Published:2026-02-03
  • Contact: Cong WANG E-mail:13504465154@163.com;wangcong2020@jlu.edu.cn

RICH HTML

 0   

PDF (PC)

8

Abstract:

To improve the classification accuracy of fault detection for wind-turbine blades， a machine-learning-based fault detection and classification method is proposed. First， a Ridge-regression-enhanced Brain Storm Optimization （R-BSO） feature-selection algorithm is developed to identify an optimal feature subset. The best feature combination extracted by R-BSO is then fed into a Stacking-based classifier to produce the final prediction， completing the RBVS blade-fault detection framework. Finally， a convolutional neural network equipped with a Convolutional Block Attention Module （CBAM）， termed CBCNN， is introduced for blade-fault classification. Experimental results demonstrate that the proposed algorithms achieve superior performance in both detection and classification of wind-turbine blade faults..

Key words: feature selection, machine learning, stacking, convolutional neural network, convolutional block attention module（CBAM）

CLC Number: 

  • TP274

Fig.1

Structure of the R-BSO algorithm"

Fig.2

Stacking strategy structure diagram"

Fig.3

Structure of RBVS algorithm"

Fig.4

Time-frequency plot of vibration signal"

Fig.5

Structure of CNN algorithm"

Fig.6

Structure of S-CNN"

Fig.7

CBAM module structure"

Fig.8

CBCNN algorithm structure diagram"

Fig.9

ROC curve comparison between R-BSO algorithm and other algorithms"

Fig.10

PRC curve comparison between R-BSO algorithm and other algorithms"

Table 1

Performance comparison between R-BSOalgorithm and other algorithms"

特征算法AccuracyPrecisionRecallF1-score
R-BSO0.893 70.894 40.893 50.899 8
RFE0.843 70.826 40.872 60.848 9
LASSO0.851 50.847 00.860 20.853 6
Ridge0.884 30.887 50.881 90.884 7
BSO0.887 50.876 50.893 20.889 9
ReliefF0.867 10.855 80.885 00.870 2

Fig.11

Comparison of ROC curves between RBVSand contrast algorithms"

Fig.12

Comparison of PRC curves between RBVSand contrast algorithms"

Table 2

Fault detection performance of RBVS algorithm"

评价指标RBVS随机森林XGBoost极度随机树ReliefF RFRFXGBLGBXGB
Accuracy0.938 40.909 80.869 60.924 10.915 20.908 00.912 5
Precision0.932 90.923 10.879 10.922 30.918 10.907 10.907 9
Recall0.950 40.902 60.870 10.933 30.919 70.917 90.926 5
F1-score0.941 60.912 70.874 60.927 80.918 90.912 50.917 1
AUROC0.981 40.967 90.951 90.968 40.971 50.970 40.971 2
AUPRC0.979 10.965 40.951 60.962 90.970 30.970 80.971 9

Fig.13

Model loss and model accuracy change between CBCNN algorithm and other algorithms"

Table 3

Simulation results of accuracy"

CBCNNS-CNNLSTMCNN
验证集0.990 00.880 60.689 60.596 5
测试集0.901 30.816 50.668 70.536 5
[1] Anindya G, Sundaresan Mannur J, Schulz Mark J, et al. Structural health monitoring techniques for wind turbine blades[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2000, 85(3): 309-324.
[2] Mayes R L. An experimental algorithm for detecting damage applied to the I-40 bridge over the Rio-grande [C]∥Smart Systems for Bridges, Structures, and Highways Conference on Smart Structures and Materials, Nashville, Tennessee, 1995: 219-225.
[3] Dervilis N, Choi M, Taylor S G, et al. On damage diagnosis for a wind turbine blade using pattern recognition[J]. Journal of Sound and Vibration, 2014, 333(6): 1833-1850.
[4] Sohn H, Worden K, Farrar C R. Novelty detection under changing environmental conditions[C]∥Smart Systems for Bridges, Structures, and Highways Conference on Smart Structures and Materials, Newport Beach, USA, 2001: 108-118.
[5] Scholz M, Vigario R. Nonlinear PCA: a new hierarchical approach[C]∥The 10th European Symposium on Artificial Neural Networks, Bruges, Belgium, 2002:439-444.
[6] Wang M H, Lu S D, Hsieh C C, et al. Fault detection of wind turbine blades using multi-channel CNN[J]. Sustainability (Switzerland), 2022, 14(3): No.1781.
[7] 宋柬霏. 小样本(医疗)数据的群智能特征选择算法研究[D]. 长春: 吉林大学通信工程学院, 2023.
Song Jian-fei. Research on swarm intelligence feature selection algorithm for small sample (medical) data[D]. Changchun: College of Communication Engineering, Jilin University, 2023.
[8] Jia H M, Li J D, Song W L, et al. Spotted hyena optimization algorithm with simulated annealing for feature selection[J]. IEEE Access, 2019, 7: 71943-71962.
[9] Ou Y W, Tatsis K E. Vibration-based monitoring of a small-scale wind turbine blade under varying climate conditions. part Ⅰ: an experimental benchmark[J]. Structural Control & Health Monitoring, 2021,28(6): No.2660.
[10] Philipp G, Lukas L. Non-uniqueness theory in sampled STFT phase retrieval[J]. SIAM Journal on Mathematical Analysis, 2023, 55(5): 4695-4726.
[11] Gai J X, Zhang L H, Wei Z H. Spectrum sensing based on STFT-ImpResNet for cognitive radio[J]. Electronics, 2022, 11(15): No.2437.
[12] 姚欣宜. 地面运动目标振动感知与识别方法研究[D]. 长春: 吉林大学通信工程学院, 2022.
Yao Xin-yi. Research on moving ground-target perception and recognition method based on vibration[D]. Changchun: College of Communication Engineering, Jilin University, 2022.
[13] 李婷, 付德义, 薛扬. 基于AE与STFT的变桨轴承裂纹诊断研究[J]. 振动、测试与诊断, 2021, 41(2): 299-303.
Li Ting, Fu De-yi, Xue Yang. Research on crack diagnosis of pitch bearing based on AE and STFT[J]. Journal of Vibration, Measurement & Diagnosis, 2021, 42(2): 299-303.
[14] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]∥ Advances in Neural Information Processing Systems, Lake Tahoe, USA, 2012: 1097-1105.
[15] Hubel D H, Wiesel T N. Receptive fields, binocular interaction and functional architecture in the cat´s visual cortex[J]. The Journal of Physiology, 1962, 160(1): 106-154.
[16] Zhou Z H, Wu J, Tang W. Ensembling neural networks: many could be better than all[J]. Artificial Intelligence, 2002, 137(1-2): 239-263.
[17] Vinay M B, Rekha K S. A model of saliency-based visual attention for rapid scene analysis[J]. International Journal of Recent Technology and Engineering, 2019, 7(6): 412-415.
[18] 冀泽宇. 分布式轻量化非侵入式电力负荷监测方法研究[D]. 长春: 吉林大学通信工程学院, 2023.
Ji Ze-yu. Research on distributed lightweight non-intrusive power load monitoring method[D]. Changchun: College of Communication Engineering, Jilin University, 2023.
[19] 高广尚. 深度学习推荐模型中的注意力机制研究综述[J]. 计算机工程与应用, 2022, 58(9): 9-18.
Gao Guang-shang. Survey on attention mechanisms in deep learning recommendation models[J]. Computer Engineering and Applications, 2022, 58(9): 9-18.
[20] Hu J, Shen L, Albanie S, et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8): 2011-2023.
[21] Sanghyun W, Jongchan P, Joon-young L, et al. CBAM: convolutional block attention module[J].Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 11211: 3-19.
[22] 王涛. 基于随机森林的机载设备异常振动故障检测方法[J]. 电子设计工程, 2023, 31(8): 119-122, 127.
Wang Tao. Abnormal vibration fault detection method of airborne equipment based on random forest[J]. Electronic Design Engineering,2023,31(8): 119-122, 127.
[23] 马康原, 李忠虎, 王金明, 等. 基于XGBoost模型的风电机组故障检测[J].内蒙古科技大学学报, 2023(4): 349-353.
Ma Kang-yuan, Li Zhong-hu, Wang Jin-ming, et al. Wind turbine fault detection based on XGBoost model[J]. Journal of Inner Mongolia University of Science and Technology,2023(4): 349-353.
[24] 陈宇韬, 唐明珠, 吴华伟, 等.基于极端随机森林的大型风电机组发电机故障检测[J].湖南电力, 2019, 39(6): 45-51.
Chen Yu-tao, Tang Ming-zhu, Wu Hua-wei, et al. Fault detection based on extreme random forest for large wind turbine generators[J]. Hunan Electric Power, 2019, 39(6): 45-51.
[25] 梁川. 基于特征提取和随机森林的风机故障诊断[J].科学技术创新, 2020(26): 55-58.
Liang Chuan. Wind turbine fault diagnosis based on feature extraction and random forest[J]. Scientific and Technological Innovation, 2020(26): 55-58.
[26] 张好雨. 基于SCADA数据的风机叶片覆冰故障检测与预测[D]. 北京: 北京交通大学机械工程学院,2022.
Zhang Hao-yu. Detection and prediction of wind turbine blade icing fault based on SCADA data[D]. Beijing: College of Mechanical Engineering, Beijing Jiaotong University, 2022.
[27] 杨亚男. 基于SCADA数据的风机叶片故障诊断系统研究[D]. 北京: 华北电力大学控制与计算机工程学院, 2020.
Yang Ya-nan. Research on fan blade fault diagnosis system based on SCADA data[D]. Beijing: College of Control and Computer Engineering, North China Electric Power University, 2020.
[28] 刘天奇, 贾明峰, 赵薇. 基于振动信号的风机叶片故障检测[J]. 电力设备管理, 2023(20): 90-92.
Liu Tian-qi, Jia Ming-feng, Zhao Wei. Vibration signal-based fault detection of wind turbine blades[J]. Power equipment management, 2023(20): 90-92.
[29] 于嵩浩. 风力发电机组叶片健康智能声学监测系统[D]. 北京: 北京邮电大学自动化学院, 2020.
Yu Song-hao. Intelligent acoustic monitoring system for wind turbine blade health[D]. Beijing: School of Automation, Beijing University of Posts and Telecommunications, 2020.
[1] Yue HOU,Xin ZHANG,Yue WU. Traffic flow prediction based on spatio-temporal dynamic constraint graph feedback [J]. Journal of Jilin University(Engineering and Technology Edition), 2026, 56(1): 183-198.
[2] Jing TIAN,She-qiang MA,Xian-min SONG,Dan ZHAO,Fa-cheng CHEN. Adaptive convolutional network for traffic state estimation under sparse data [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(8): 2579-2587.
[3] Hong XIAO,Xian-de LIU. Real-time acquisition and dynamic analysis of learning state based on hybrid intelligence [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(7): 2402-2408.
[4] Zhi-gang FENG,Shou-qi WANG,Ming-yue YU. Rolling bearing fault diagnosis based on variational mode extraction and lightweight network [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(6): 1883-1891.
[5] Feng-feng ZHOU,Zhe GUO,Yu-si FAN. Feature representation algorithm for imbalanced classification of multi⁃omics cancer data [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(6): 2089-2096.
[6] Rui-shan DU,Zi-shan WANG. Multi perspective facial expression recognition algorithm based on spatiotemporal attention [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(6): 2097-2102.
[7] Sheng-qi MEI,Xiao-dong LIU,Xing-ju WANG,Xu-feng LI,Teng WU,Xiang-xu CHENG. Prediction of high strength concrete creep based on parametric MIC analysis and machine learning algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(5): 1595-1603.
[8] Xiu-zhi ZHAO,De-hong XIE. Discrimination method for Pu-er tea varieties based on noise-robust feature extraction [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(5): 1756-1762.
[9] Qing-liang JIN,Xin-sen ZHOU,Yi CHEN,Cheng-wen WU. Predictive model for identifying innovative university talents based on the swarm intelligence evolution enhanced kernel extreme learning machine [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(5): 1763-1771.
[10] Jun WANG,Chang-fu SI,Kai-peng WANG,Qiang FU. Intrusion detection method based on ensemble learning and feature selection by PSO-GA [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(4): 1396-1405.
[11] Xiang-hai MENG,Guo-rui WANG,Ming-yang ZHANG,Bi-jiang TIAN. Traffic accident prediction model of mountain highways based on selection integration [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(4): 1298-1306.
[12] Yin-fei DAI,Xiu-zhen ZHOU,Yu-bao LIU,Zhi-yuan LIU. In⁃vehicle network intrusion detection system based on CAN bus data [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(3): 857-865.
[13] Wei-wei LUO,Chang-long LIU,Qin LEI. Spatial image steganography based on preprocessing layer enhancement and attention mechanism [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(12): 4024-4033.
[14] Hao WANG,Bin ZHAO,Guo-hua LIU. Temporal and motion enhancement for video action recognition [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(1): 339-346.
[15] Lei ZHANG,Jing JIAO,Bo-xin LI,Yan-jie ZHOU. Large capacity semi structured data extraction algorithm combining machine learning and deep learning [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(9): 2631-2637.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd