Purpose

The purpose of this paper is to recognize and label the faults in wind turbines with a new density-based clustering algorithm, named contour density scanning clustering (CDSC) algorithm.

Design/methodology/approach

The algorithm includes four components: (1) computation of neighborhood density, (2) selection of core and noise data, (3) scanning core data and (4) updating clusters. The proposed algorithm considers the relationship between neighborhood data points according to a contour density scanning strategy.

Findings

The first experiment is conducted with artificial data to validate that the proposed CDSC algorithm is suitable for handling data points with arbitrary shapes. The second experiment with industrial gearbox vibration data is carried out to demonstrate that the time complexity and accuracy of the proposed CDSC algorithm in comparison with other conventional clustering algorithms, including k-means, density-based spatial clustering of applications with noise, density peaking clustering, neighborhood grid clustering, support vector clustering, random forest, core fusion-based density peak clustering, AdaBoost and extreme gradient boosting. The third experiment is conducted with an industrial bearing vibration data set to highlight that the CDSC algorithm can automatically track the emerging fault patterns of bearing in wind turbines over time.

Originality/value

Data points with different densities are clustered using three strategies: direct density reachability, density reachability and density connectivity. A contours density scanning strategy is proposed to determine whether the data points with the same density belong to one cluster. The proposed CDSC algorithm achieves automatically clustering, which means that the trends of the fault pattern could be tracked.

中文翻译：

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使用基于密度的聚类算法识别和标记风力发电机组中的故障

目的

本文的目的是使用一种新的基于密度的聚类算法——轮廓密度扫描聚类（CDSC）算法来识别和标记风力发电机组中的故障。

设计/方法/方法

该算法包括四个组成部分：（1）邻域密度的计算，（2）核心和噪声数据的选择，（3）扫描核心数据和（4）更新集群。所提出的算法根据轮廓密度扫描策略考虑邻域数据点之间的关系。

发现

第一个实验是用人工数据进行的，以验证所提出的 CDSC 算法适用于处理任意形状的数据点。对工业齿轮箱振动数据进行的第二个实验证明了所提出的 CDSC 算法与其他传统聚类算法（包括k-means）相比的时间复杂度和准确性, 基于密度的噪声应用空间聚类、密度峰值聚类、邻域网格聚类、支持向量聚类、随机森林、基于核心融合的密度峰值聚类、AdaBoost 和极端梯度提升。第三个实验是使用工业轴承振动数据集进行的，以强调 CDSC 算法可以随着时间的推移自动跟踪风力涡轮机轴承出现的故障模式。

原创性/价值

使用三种策略对具有不同密度的数据点进行聚类：直接密度可达性、密度可达性和密度连通性。提出了一种轮廓密度扫描策略来确定具有相同密度的数据点是否属于一个簇。所提出的CDSC算法实现了自动聚类，这意味着可以跟踪故障模式的趋势。