An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

Tang, Mingzhu; Zhao, Qi; Ding, Steven X.; Wu, Huawei; Li, Linlin; Wen, Long; Huang, Bin

doi:10.3390/en13040807

Cited by 80 publications

(43 citation statements)

References 34 publications

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“…To conclude, the optimal objective function can be solved through minimization of the quadratic function. Detailed descriptions of LGBMR have been provided in previous studies [39,41,63].…”

Section: Lgbmrmentioning

confidence: 99%

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Prediction of River Stage Using Multistep-Ahead Machine Learning Techniques for a Tidal River of Taiwan

Guo

Chen

Yeh

et al. 2021

Water

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Time-series prediction of a river stage during typhoons or storms is essential for flood control or flood disaster prevention. Data-driven models using machine learning (ML) techniques have become an attractive and effective approach to modeling and analyzing river stage dynamics. However, relatively new ML techniques, such as the light gradient boosting machine regression (LGBMR), have rarely been applied to predict the river stage in a tidal river. In this study, data-driven ML models were developed under a multistep-ahead prediction framework and evaluated for river stage modeling. Four ML techniques, namely support vector regression (SVR), random forest regression (RFR), multilayer perceptron regression (MLPR), and LGBMR, were employed to establish data-driven ML models with Bayesian optimization. The models were applied to simulate river stage hydrographs of the tidal reach of the Lan-Yang River Basin in Northeastern Taiwan. Historical measurements of rainfall, river stages, and tidal levels were collected from 2004 to 2017 and used for training and validation of the four models. Four scenarios were used to investigate the effect of the combinations of input variables on river stage predictions. The results indicated that (1) the tidal level at a previous stage significantly affected the prediction results; (2) the LGBMR model achieves more favorable prediction performance than the SVR, RFR, and MLPR models; and (3) the LGBMR model could efficiently and accurately predict the 1–6-h river stage in the tidal river. This study provides an extensive and insightful comparison of four data-driven ML models for river stage forecasting that can be helpful for model selection and flood mitigation.

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“…To conclude, the optimal objective function can be solved through minimization of the quadratic function. Detailed descriptions of LGBMR have been provided in previous studies [39,41,63].…”

Section: Lgbmrmentioning

confidence: 99%

“…In this study, Bayesian optimization was employed and combined with 10-fold crossvalidation to enhance the prediction accuracy. Bayesian optimization was implemented for model training according to the following steps [39,40]:…”

Section: Bayesian Optimization and Cross-validationmentioning

confidence: 99%

Prediction of River Stage Using Multistep-Ahead Machine Learning Techniques for a Tidal River of Taiwan

Guo

Chen

Yeh

et al. 2021

Water

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“…To evaluate the fault detection performance of the model, a confusion matrix [29] is introduced, as defined in Table 1.…”

Section: Evaluation Criteria For Fault Detection Performancementioning

confidence: 99%

Fault Detection of Wind Turbine Pitch System Based on Multiclass Optimal Margin Distribution Machine

Tang

Kuang

Zhao

et al. 2020

Mathematical Problems in Engineering

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In response to the unbalanced sample categories and complex sample distribution of the operating data of the pitch system of the wind turbine generator system, this paper proposes a method for fault detection of the pitch system of the wind turbine generator system based on the multiclass optimal margin distribution machine. In this method, the power output of the wind turbine generator system is used as the main status parameter, and the operating data history of the wind turbine generator system in the wind power supervisory control and data acquisition (SCADA) system is subject to correlation analysis with the Pearson correlation coefficient, to eliminate the features that have low correlation with the power output status parameter. Secondary analysis is performed to the remaining features, thus reducing the number and complexity of samples. Datasets are divided into the training set for training of the multiclass optimal margin distribution machine fault detection model and test set for testing. Experimental verification was carried out with the operating data of one wind farm in China. Experimental results show that, compared with other support vector machines, the proposed method has higher fault detection accuracy and precision and lower false-negative rate and false-positive rate.

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“…Because a failure is a process rather than an event, the earlier the process is detected, the more the flexibility that exists to manage it. Fault detection strategies usually warn about the appearance of a fault too late, and the fault is already mature when it is detected, which prevents proper planning of the maintenance operation [ 3 , 4 , 5 ]. Meanwhile, prognosis strategies provide a predictive maintenance option that gives the decision-maker the flexibility to decide whether and when to act before the subsystem or turbine fails.…”

Section: Introductionmentioning

confidence: 99%

Wind Turbine Main Bearing Fault Prognosis Based Solely on SCADA Data

Encalada-Dávila

Puruncajas

Tutivén

et al. 2021

Sensors

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As stated by the European Academy of Wind Energy (EAWE), the wind industry has identified main bearing failures as a critical issue in terms of increasing wind turbine reliability and availability. This is owing to major repairs with high replacement costs and long downtime periods associated with main bearing failures. Thus, the main bearing fault prognosis has become an economically relevant topic and is a technical challenge. In this work, a data-based methodology for fault prognosis is presented. The main contributions of this work are as follows: (i) Prognosis is achieved by using only supervisory control and data acquisition (SCADA) data, which is already available in all industrial-sized wind turbines; thus, no extra sensors that are designed for a specific purpose need to be installed. (ii) The proposed method only requires healthy data to be collected; thus, it can be applied to any wind farm even when no faulty data has been recorded. (iii) The proposed algorithm works under different and varying operating and environmental conditions. (iv) The validity and performance of the established methodology is demonstrated on a real underproduction wind farm consisting of 12 wind turbines. The obtained results show that advanced prognostic systems based solely on SCADA data can predict failures several months prior to their occurrence and allow wind turbine operators to plan their operations.

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An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

Cited by 80 publications

References 34 publications

Prediction of River Stage Using Multistep-Ahead Machine Learning Techniques for a Tidal River of Taiwan

Prediction of River Stage Using Multistep-Ahead Machine Learning Techniques for a Tidal River of Taiwan

Fault Detection of Wind Turbine Pitch System Based on Multiclass Optimal Margin Distribution Machine

Wind Turbine Main Bearing Fault Prognosis Based Solely on SCADA Data

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