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Recognition and labeling of faults in wind turbines with a density-based clustering algorithm

Shuai Luo (College of Management and Economics, Tianjin University, Tianjin, China)
Hongwei Liu (College of Management and Economics, Tianjin University, Tianjin, China)
Ershi Qi (College of Management and Economics, Tianjin University, Tianjin, China)

Data Technologies and Applications

ISSN: 2514-9288

Article publication date: 18 June 2021

Issue publication date: 11 October 2021

146

Abstract

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.

Keywords

Acknowledgements

This research was supported in part by Major Program of National Fund of Philosophy and Social Science of China (Grant number: 15ZDB151) and National Fund of Philosophy and Social Science of China (Grant number: 16BGL001).

Citation

Luo, S., Liu, H. and Qi, E. (2021), "Recognition and labeling of faults in wind turbines with a density-based clustering algorithm", Data Technologies and Applications, Vol. 55 No. 5, pp. 841-868. https://doi.org/10.1108/DTA-09-2020-0223

Publisher

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Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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