Cost-Sensitive LightGBM-Based Online Fault Detection Method for Wind Turbine Gearboxes

Tang, Mingzhu; Zhao, Qi; Wu, Huawei; Wang, Zimin

doi:10.3389/fenrg.2021.701574

Cited by 9 publications

(2 citation statements)

References 29 publications

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“…In recent years, CSL has been greatly applied in Light-GBM. Tang et al proposed a cost-sensitive LightGBM-based online fault detection model to optimize the misclassification loss [17]. Liu et al proposed a cost-sensitive Light-GBM (LightGBM-focal) for the imbalanced credit scoring problem [18].…”

Section: Lightgbm With Cost-sensitive Learning (Csl)mentioning

confidence: 99%

Cost Harmonization LightGBM-Based Stock Market Prediction

Zhao,

Liu,

Zhao

2023

IEEE Access

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Stock market prediction (SMP) is a challenging task due to its uncertainty, nonlinearity, and volatility. Machine learning models, such as artificial neural networks (ANNs) and support vector regression (SVR), have been widely used for stock market prediction and achieved high performance in the sense of "minimum errors." In the context of SMP, however, it is more meaningful to measure the performance using "minimum cost." For example, a false positive error (FPE) could result in a big trading loss, while a false negative error (FNE) might just miss a chance. For a "cautious" investor, fewer FPEs are preferable. In fact, cost-sensitive learning has been used in areas such as fraud detection and medical diagnosis. In our earlier study, we proposed a false-sensitive method called focal-loss LightBGM (FL-LightGBM) for SMP by introducing a cost-aware loss in LightGBM, which is known to be a fast and efficient gradientboosting learning algorithm for solving large-scale problems. FL-LightBGM, however, still assumes that all false negative errors (or false positive errors) contribute equally to the final cost. Such learned trading strategies might be useful only for an investor who is always "aggressive" or "cautious." In practice, some errors may result in irreversible loss, so it is important to measure the cost based on "data" rather than the investor's character. In this paper, we propose a new method called cost-harmonization loss-based LightGBM (CHL-LightGBM), in which the cost for each datum can be calculated dynamically based on the difficulty of the datum. To verify the effectiveness of CHL-LightGBM, comparisons have been made among LightGBM, XGBoost, decision trees, FL-LightGBM, and CHL-LightGBM for stock predictions on data from Shanghai, Hong Kong, and NASDAQ Stock Exchanges. The simulation results show that although there is no significant difference between CHL-LightGBM and other models on the accuracy and winning rate, CHL-LightGBM obtained the highest annual return on all the test data.

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Section: Lightgbm With Cost-sensitive Learning (Csl)mentioning

confidence: 99%

Cost Harmonization LightGBM-Based Stock Market Prediction

Zhao,

Liu,

Zhao

2023

IEEE Access

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“…Currently, research on fault detection of wind turbine gearboxes mainly includes methods based on signal processing, along with data-driven and model-based methods [6][7][8]. Signal-based approaches-such as spectral analysis, wavelet transform [9], and non-parametric spectrum estimation-are often carried out.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection of Wind Turbine Gearboxes Based on IBOA-ERF

Tang

Cao

et al. 2022

Sensors

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As one of the key components of wind turbines, gearboxes are under complex alternating loads for a long time, and the safety and reliability of the whole machine are often affected by the failure of internal gears and bearings. Aiming at the difficulty of optimizing the parameters of wind turbine gearbox fault detection models based on extreme random forest, a fault detection model with extreme random forest optimized by the improved butterfly optimization algorithm (IBOA-ERF) is proposed. The algebraic sum of the false alarm rate and the missing alarm rate of the fault detection model is constructed as the fitness function, and the initial position and position update strategy of the individual are improved. A chaotic mapping strategy is introduced to replace the original population initialization method to enhance the randomness of the initial population distribution. An adaptive inertia weight factor is proposed, combined with the landmark operator of the pigeon swarm optimization algorithm to update the population position iteration equation to speed up the convergence speed and improve the diversity and robustness of the butterfly optimization algorithm. The dynamic switching method of local and global search stages is adopted to achieve dynamic balance between global exploration and local search, and to avoid falling into local optima. The ERF fault detection model is trained, and the improved butterfly optimization algorithm is used to obtain optimal parameters to achieve fast response of the proposed model with good robustness and generalization under high-dimensional data. The experimental results show that, compared with other optimization algorithms, the proposed fault detection method of wind turbine gearboxes has a lower false alarm rate and missing alarm rate.

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A new ensemble classification approach based on Rotation Forest and LightGBM

Sun

et al. 2023

Neural Comput & Applic

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Cost-Sensitive LightGBM-Based Online Fault Detection Method for Wind Turbine Gearboxes

Cited by 9 publications

References 29 publications

Cost Harmonization LightGBM-Based Stock Market Prediction

Cost Harmonization LightGBM-Based Stock Market Prediction

Fault Detection of Wind Turbine Gearboxes Based on IBOA-ERF

A new ensemble classification approach based on Rotation Forest and LightGBM

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