A multi-scale feature fusion network-based fault diagnosis method for wind turbine bearings

Ma, Minghan; Hou, Yuejia; Li, Yonggang

doi:10.1177/0309524x221114621

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Gao et al [23] proposed a composite fault diagnosis method for rolling bearings based on parameter-optimized maximum correlation kurtosis deconvolution and a CNN. Ma et al [24] proposed a fault diagnosis method based on a multiscale feature fusion network (MSFF-CNN). These methods achieve end-to-end recognition with high accuracy, but the number of parameters is large and the running time is long.…”

Section: Introductionmentioning

confidence: 99%

A lightweight model for train bearing fault diagnosis based on multiscale attentional feature fusion

He¹,

He²,

Lao³

et al. 2022

Meas. Sci. Technol.

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As one of the key components of a train, the running gear bearing has the highest fault rate, and its health condition is very important for the safe operation of the train. Therefore, how to quickly and accurately diagnose the health condition of the train running gear bearings under strong noise and variable working conditions has become one of the core contents of the intelligent operation and maintenance strategy. To meet these requirements, a lightweight convolutional neural network based on multiscale attentional feature fusion (MA-LCNN) is proposed in this paper, which takes the inverted residual network as the main structure. Firstly, a multiscale attention module (MA) was designed to extract fault feature information. Secondly, by embedding MAs in different locations, the ability of the MA-LCNN to extract fault feature information is greatly improved. Finally, an ablation experiment and noise resistance experiment are performed. The recognition accuracy scores of the MA-LCNN for cases 2 and 3 are 99.70% and 99.83%, respectively. The results show that the proposed attention module has better learning ability and stability compared to the contrast modules. The MA-LCNN demonstrates better fault diagnosis performance than contrast models under different noise environments and variable working conditions.

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Section: Introductionmentioning

confidence: 99%

A lightweight model for train bearing fault diagnosis based on multiscale attentional feature fusion

He¹,

He²,

Lao³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

An Online Fault Detection and Remaining Life Prediction Method Based on SVDD for Rolling Bearings

Yan,

Zhao,

et al. 2024

IEEE Trans. Instrum. Meas.

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Temperature rise of high-speed bearing in gearbox of 5 MW wind turbine based on Bayesian-LightGBM and improved PSO-SVM troubleshooting

Tang,

Tao,

Qiu

et al. 2024

Measurement and Control

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5MW wind turbine gearbox high-speed bearing temperature rise failure is one of the important factors affecting the stable operation of the wind turbine, accurate prediction and timely diagnosis can effectively improve the efficiency of the wind turbine. In this paper, a combined modelling wind turbine gearbox high-speed bearing temperature rise prediction method based on Bayesian-LightGBM and improved PSO-SVM is proposed with a 5 MW wind turbine as the research object. Firstly, the initial dimensionality reduction of SCADA data is performed by sparse random projection matrix, which reduces the redundant data. Secondly, feature selection is performed on the remaining data using Bayesian-LightGBM to identify 13 key input feature parameters. Then, the hyperparameters of the PSO algorithm are optimised using Bayesian algorithm and further, the optimised PSO algorithm is applied to identify the SVM parameters. Finally, a simulation experiment platform is established based on MATLAB to verify the temperature rise of high-speed bearings in gearboxes of wind turbines by example calculation and comparative analysis. The results show that the model established in this paper is effective, the prediction results are accurate and the performance is stable, and then compared with the algorithms such as PSO-SVM, SVM, BP, etc, the coefficient of determination of the algorithm is greater than 0.994 in both the training set and the test set, and the average decision percentage error is around 1.88% in both.

show abstract

A multi-scale feature fusion network-based fault diagnosis method for wind turbine bearings

Cited by 3 publications

References 17 publications

A lightweight model for train bearing fault diagnosis based on multiscale attentional feature fusion

A lightweight model for train bearing fault diagnosis based on multiscale attentional feature fusion

An Online Fault Detection and Remaining Life Prediction Method Based on SVDD for Rolling Bearings

Temperature rise of high-speed bearing in gearbox of 5 MW wind turbine based on Bayesian-LightGBM and improved PSO-SVM troubleshooting

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