A new condition-monitoring method based on multi-variable correlation learning network for wind turbine fault detection

Yao, Qingtao; Li, Xianze; Xiang, Ling; Aijun, Hu; Bing, Hankun

doi:10.1088/1361-6501/ac9f5d

Cited by 8 publications

(5 citation statements)

References 31 publications

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“…The accuracy of prediction directly affects the reliability and efficiency of the wind turbine system operating state. For example, some researchers [38] have proposed a multivariate correlation learning network based on the attention mechanism for extracting the features of wind turbine state monitoring data. Furthermore, the complex relationship between the features and the outputs was initially obtained with the vibration signal as a parameter.…”

Section: Previous Related Workmentioning

confidence: 99%

State Reliability of Wind Turbines Based on XGBoost–LSTM and Their Application in Northeast China

Gou,

Zhang,

Wen

et al. 2024

Sustainability

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The use of renewable energy sources, such as wind power, has received more attention in China, and wind turbine system reliability has become more important. Based on existing research, this study proposes a state reliability prediction model for wind turbine systems based on XGBoost–LSTM. By considering the dynamic variability of the weight fused by the algorithm, under the irregular fluctuation of the same parameter with time in nonlinear systems, it reduces the algorithm defects in the prediction process. The improved algorithm is validated by arithmetic examples, and the results show that the root mean square error value (hereinafter abbreviated as RMSE) and the mean absolute error value (hereinafter abbreviated as MAPE) of the improved XGBoost–LSTM algorithm are decreased compared with those for the LSTM and XGBoost algorithms, among which the RMSE is reduced by 8.26% and 4.15% and the MAPE is reduced by 24.56% and 27.99%, respectively; its goodness-of-fit R2 value is closer to 1. This indicates that the algorithm proposed in this paper reduces the existing defects present in some current algorithms, and the prediction accuracy is effectively improved, which is of great value in improving the reliability of the system.

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Section: Previous Related Workmentioning

confidence: 99%

State Reliability of Wind Turbines Based on XGBoost–LSTM and Their Application in Northeast China

Gou,

Zhang,

Wen

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…However, traditional RNN is easily plagued by the problems of long-term dependence and gradient explosion. Accordingly, some RNN variants have been investigated for solving the above problems and further improving network performance [24][25][26], among which GRU has received widespread attention. Zhou et al [27] proposed a novel dual-thread GRU architecture to extract both static and non-static information from input data and capture the hidden state difference between two consecutive time steps.…”

Section: Introductionmentioning

confidence: 99%

Multi-scale residual neural network with enhanced gated recurrent unit for fault diagnosis of rolling bearing

Liao,

Fu,

Yang

et al. 2024

Meas. Sci. Technol.

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As the key component of rotating machinery, effective and reliable fault diagnosis of rolling bearing is particularly critical for promoting production safety and economic benefits. The powerful representation learning ability of convolutional neural network (CNN) enables it to effectively extract fault information from vibration signals of rolling bearing. Nevertheless, challenges are faced by CNN in extracting features at multi-scale and capturing temporal features. With regard to this issue, a hybrid deep learning model that incorporates the multi-scale residual neural network (MSRN) with the enhanced gated recurrent unit (EGRU), namely MSRN-EGRU, is proposed in this paper. To begin with, MSRN is designed by introducing multi-scale structure and residual connections into CNN for extracting local features effectively and improving the feature representation of the model. Then, the extracted local features are input into EGRU to further extract temporal features, where EGRU is proposed by improving gated recurrent unit (GRU) structure and embeding scaled exponential liner unit (SELU), which enhances the nonlinear modeling and memory ability. Eventually, the obtained features are processed by α-Dropout and global average pooling (GAP) before being inputted into the softmax layer for fault diagnosis. To validate the effectiveness of the proposed model, three baseline models and two ablation models were employed for comparative experiments with two bearing datasets. The experimental results reveal that the proposed model achieves commendable performance in terms of accuracy, robustness, and convergence for fault diagnosis of rolling bearing.

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“…Xiang et al successively constructed CNN-LSTM-AM [17] and CNN-BiGRU-AM [18] (bidirectional gated recurrent unit) by embedding AM in neural networks, effectively improving the ability of the model to extract complex and multi-directional spatiotemporal features. Yao et al [19] integrated a plug-and-play lightweight convolutional block attention mechanism into a temporal convolutional neural network (TCN). This integration enables the calculation of attention feature maps in both temporal and spatial dimensions.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection of wind turbine based on norm-linear-ConvNeXt-TCN

Chen,

Shao,

Wang

et al. 2024

Meas. Sci. Technol.

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The supervisory control and data acquisition (SCADA) system of wind turbines continuously collects a large amount of monitoring data during their operation. These data contain abundant information about the operating status of the turbine components. Utilizing this information makes it feasible to provide early warnings and predict the health status of the wind turbine. However, due to the strong coupling between the various components of the wind turbine, the data exhibits complex spatiotemporal relationships, multiple state parameters, strong non-linearity, and noise interference, which brings great difficulty to anomaly detection of the wind turbine. This paper proposes a new method for detecting abnormal operating conditions of wind turbines, based on a cleverly designed multi-layer Linear residual module and the improved Temporal Convolutional Network (TCN) with a new ConvNeXt architecture (NLC-TCN). Initially, the NLC-TCN deep learning reconstruction model is trained with historical data of normal behavior to extract the spatiotemporal features of state parameters under normal operational conditions. Subsequently, the condition score of the unit is determined by calculating the average NRMSE between the reconstructed data and actual data. The SPOT real-time calculation of the anomaly warning threshold, based on extreme value theory, is then used for preliminary fault monitoring. Moreover, by shielding the fault alarm for low wind speeds and implementing a continuous delay perception mechanism, issues related to wind speed fluctuations and internal and external interference are addressed, enabling early warning for faulty units. Finally, the effectiveness and reliability of the proposed method are validated through comparative experiments using actual offshore wind farm SCADA data. The performance of the proposed method surpasses that of other compared methods. Additionally, the results of the proposed method were evaluated using the UMAP dimensionality reduction technique and kernel density estimation.

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A new condition-monitoring method based on multi-variable correlation learning network for wind turbine fault detection

Cited by 8 publications

References 31 publications

State Reliability of Wind Turbines Based on XGBoost–LSTM and Their Application in Northeast China

State Reliability of Wind Turbines Based on XGBoost–LSTM and Their Application in Northeast China

Multi-scale residual neural network with enhanced gated recurrent unit for fault diagnosis of rolling bearing

Anomaly detection of wind turbine based on norm-linear-ConvNeXt-TCN

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