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首页> 外文期刊>Mechanical systems and signal processing >Wind turbine fault detection and classification by means of image texture analysis
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Wind turbine fault detection and classification by means of image texture analysis

机译：通过图像纹理分析的风力发电机故障检测与分类

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摘要

The future of the wind energy industry passes through the use of larger and more flexible wind turbines in remote locations, which are increasingly offshore to benefit stronger and more uniform wind conditions. The cost of operation and maintenance of offshore wind turbines is approximately 15–35% of the total cost. Of this, 80% goes towards unplanned maintenance issues due to different faults in the wind turbine components. Thus, an auspicious way to contribute to the increasing demands and challenges is by applying low-cost advanced fault detection schemes. This work proposes a new method for detection and classification of wind turbine actuators and sensors faults in variable-speed wind turbines. For this purpose, time domain signals acquired from the operating wind turbine are represented as two-dimensional matrices to obtain grayscale digital images. Then, the image pattern recognition is processed getting texture features under a multichannel representation. In this work, four types of texture characteristics are used: statistical, wavelet, granulometric and Gabor features. Next, the most significant ones are selected using the conditional mutual criterion. Finally, the faults are detected and distinguished between them (classified) using an automatic classification tool. In particular, a 10-fold cross-validation is used to obtain a more generalized model and evaluates the classification performance. Coupled non-linear aero-hydro-servo-elastic simulations of a 5 MW offshore type wind turbine are carried out in several fault scenarios. The results show a promising methodology able to detect and classify the most common wind turbine faults.
机译：风能行业的未来是通过在偏远地区使用更大，更灵活的风力涡轮机实现的，这些风力涡轮机越来越靠近海上，以受益于更强劲，更均匀的风况。海上风力涡轮机的运营和维护成本约为总成本的15–35％。其中80％归因于风力涡轮机组件中的不同故障而导致计划外维护。因此，一种有助于增加需求和挑战的吉祥方式是应用低成本的高级故障检测方案。这项工作提出了一种用于检测和分类变速风力涡轮机中的风力涡轮机致动器和传感器故障的新方法。为此，将从运行中的风力涡轮机获取的时域信号表示为二维矩阵，以获得灰度数字图像。然后，在多通道表示下处理图像图案识别以获得纹理特征。在这项工作中，使用了四种类型的纹理特征：统计特征，小波特征，粒度特征和Gabor特征。接下来，使用条件互惠准则选择最重要的一个。最后，使用自动分类工具检测故障并在故障之间进行分类（分类）。特别是，使用10倍交叉验证来获得更通用的模型并评估分类性能。在几种故障情况下，对5 MW海上型风力发电机组进行了非线性的航空-水-气-弹性耦合模拟。结果表明，一种有前途的方法能够检测和分类最常见的风力发电机故障。

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  • 来源
    《Mechanical systems and signal processing》 |2018年第7期|149-167|共19页
  • 作者

    Magda Ruiz; Luis E. Mujica; Santiago Alférez; Leonardo Acho; Christian Tutivén; Yolanda Vidal; José Rodellar; Francesc Pozo;

  • 作者单位

    Control Dynamics and Applications (CoDAlab), Departament de Matemàtiques, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politécnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB);

    Control Dynamics and Applications (CoDAlab), Departament de Matemàtiques, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politécnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB);

    Control Dynamics and Applications (CoDAlab), Departament de Matemàtiques, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politécnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB);

    Control Dynamics and Applications (CoDAlab), Departament de Matemàtiques, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politécnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB);

    Control Dynamics and Applications (CoDAlab), Departament de Matemàtiques, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politécnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB);

    Control Dynamics and Applications (CoDAlab), Departament de Matemàtiques, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politécnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB);

    Control Dynamics and Applications (CoDAlab), Departament de Matemàtiques, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politécnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB);

    Control Dynamics and Applications (CoDAlab), Departament de Matemàtiques, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politécnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB);

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Fault detection; Fault classification; Wind turbine; Texture analysis;

    机译：故障检测;故障分类;风轮机;纹理分析;

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