Abstract

In practice, faulty samples of wind turbine (WT) gearboxes are far smaller than normal samples during operation, and most of the existing fault diagnosis methods for WT gearboxes only focus on the improvement of classification accuracy and ignore the decrease of missed alarms and the reduction of the average cost. To this end, a new framework is proposed through combining the Spearman rank correlation feature extraction and cost-sensitive LightGBM algorithm for WT gearbox’s fault detection. In this article, features from wind turbine supervisory control and data acquisition (SCADA) systems are firstly extracted. Then, the feature selection is employed by using the expert experience and Spearman rank correlation coefficient to analyze the correlation between the big data of WT gearboxes. Moreover, the cost-sensitive LightGBM fault detection framework is established by optimizing the misclassification cost. The false alarm rate and the missed detection rate of the WT gearbox under different working conditions are finally obtained. Experiments have verified that the proposed method can significantly improve the fault detection accuracy. Meanwhile, the proposed method can consistently outperform traditional classifiers such as AdaCost, cost-sensitive GBDT, and cost-sensitive XGBoost in terms of low false alarm rate and missed detection rate. Owing to its high Matthews correlation coefficient scores and low average misclassification cost, the cost-sensitive LightGBM (CS LightGBM) method is preferred for imbalanced WT gearbox fault detection in practice.

Highlights

  • With the increase in the capacity of wind turbine assembly machines, wind power generation brings economic benefits and raised important crucial challenges related to reliability (Qiao and Lu, 2015; Wang et al, 2019)

  • The experiment verifies the effectiveness of the proposed cost-sensitive LightGBM for fault detection of wind turbine (WT) gearboxes

  • In order to further verify the superiority of the method, three advanced fault diagnosis methods were compared, including cost-sensitive AdaBoost (AdaCost), cost-sensitive gradient boosting decision tree (GBDT) (GBDTcost), and costsensitive XGBoost (XGBcost)

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Summary

INTRODUCTION

With the increase in the capacity of wind turbine assembly machines, wind power generation brings economic benefits and raised important crucial challenges related to reliability (Qiao and Lu, 2015; Wang et al, 2019). RF is used to rank the features of WTs by importance, and XGBoost trains the ensemble classifier for each specific fault This method is able to protect against overfitting, and it achieves better wind turbine fault detection results than SVM when processing multidimensional data. The maximum information coefficient analysis method is adopted to select features for the big data of WTs. The improved LightGBM is implemented by the Bayesian optimization for classification so as to diagnose the fault of WT gearbox. Normal samples are much greater than the number of fault samples in the real industrial field This means that many machine learning methods fail in dealing with imbalanced data and the majority class has higher recognition rate while the minority class fails. The experiment verifies the effectiveness and validity of the proposed method

RELATED WORK
BACKGROUND
Results and Discussion
CONCLUSION AND FUTURE WORK
DATA AVAILABILITY STATEMENT
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