Quantitative Diagnosis Method of Gearbox Under Varying Conditions Based on ARX Model and Generalized Canonical Correlation Analysis

Han, Liubang; Wang, Qidong; Jiang, Kuosheng; Xuan-yao, Wang; Zhou, Yuanyuan

doi:10.1109/access.2020.2972381

Cited by 1 publication

(1 citation statement)

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“…By analyzing the multiscale enveloping spectrogram, the fault characteristics of weak bearings can be detected and fault diagnosis of WTs can be realized. Due to the nonlinear and nonstationary characteristics of the gearbox, Han et al (2020) considered the correlation between variables and used a quantitative diagnosis method for gearbox faults based on generalized canonical correlation analysis, which can effectively identify the severity of gearbox faults under various conditions. Gao et al (2018) explained the drawbacks of the current support vector machine (SVM) algorithm and proposed the WT fault diagnosis method based on the least squares support vector machine.…”

Section: Introductionmentioning

confidence: 99%

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

Tang

Zhao

et al. 2021

Front. Energy Res.

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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.

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Section: Introductionmentioning

confidence: 99%