RETRACTED: Remaining useful life prediction of wind turbine generator based on 1D-CNN and Bi-LSTM

Xiao, Li; Zhang, Liyi; Niu, Feng; Su, Xiaoqin; Song, Wenqiang

doi:10.1016/j.ijfatigue.2022.107051

Cited by 8 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The lifetime of a wind turbine is typically 20 to 25 years, during which time it must be able to operate safely and smoothly [26]. Unfortunately, the uncertainties of harsh environmental conditions such as dust, temperature, air pressure, and unstable wind gusts can affect the severe alternating loads on the main load-bearing components of wind turbines [27,28]. The bearings are widely used in various systems of wind turbines, and they will inevitably be damaged when operating under harsh working conditions, which will lead to wind turbine failures and shutdowns, which in turn will require much time and cost for maintenance [29,30].…”

Section: Main Forms Of Wear On Wind Power Bearingsmentioning

confidence: 99%

Review of Wind Power Bearing Wear Analysis and Intelligent Lubrication Method Research

Peng,

Zhao,

Shangguan

et al. 2023

Coatings

View full text Add to dashboard Cite

With the significant penetration of wind generation, wind turbines require higher and higher lubrication performance for bearings. To improve the lubrication performance of wind power bearings, this study takes wind power bearings as the research object and comprehensively analyzes the wear forms of wind power bearings as well as intelligent lubrication methods. Firstly, the main roles and wear forms of wind turbine bearings are sorted out and analyzed. Secondly, the common lubrication problems of wind power bearings are analyzed from the bearing grease selection, lubrication mode, and lubrication status, highlighting the important influence of lubrication on bearings. Thirdly, the wind turbine bearing wisdom lubrication method research and organization, mainly including the wind power generation bearing lubrication materials, lubrication devices and monitoring methods, and other issues of research and analysis. Finally, current challenges and future development directions are summarized, which are designed to provide theoretical reference and technical support for the related research and engineering practice in the field of wind power engineering.

show abstract

Section: Main Forms Of Wear On Wind Power Bearingsmentioning

confidence: 99%

Review of Wind Power Bearing Wear Analysis and Intelligent Lubrication Method Research

Peng,

Zhao,

Shangguan

et al. 2023

Coatings

View full text Add to dashboard Cite

show abstract

“…CNNs are widely used in Machine Learning for a variety of tasks such as image processing or language recognition, its use in combination with LSTMs has been widely addressed [17][18][19][20][21][22][23] . The term convolution comes from the usage of kernels or filters to extract intrinsic characteristics from the input data, also known as features.…”

Section: D-cnn-lstmmentioning

confidence: 99%

Performance comparison of probabilistic and artificial neural network models for long-sequence generation of wind speed forecasts

Pina-Gongora¹,

Kazemtabrizi²,

Crabtree³

2022

21st Wind &Amp; Solar Integration Workshop (WIW 2022)

View full text Add to dashboard Cite

This paper presents a new method for generating long-sequence wind speed time-series forecasts for purposes of offshore wind farm asset and operations planning. Our goal is to develop a planning decision support tool with which wind farm planners and operators can make informed decisions for development and operation of future offshore wind assets considering revenue and power generation yield, as well as operation and maintenance expenditures.The proposed methodology should be computationally efficient and should be able to reliably generate accurate wind speed time-series forecasts for the required planning timescale. In this paper, we used an Autoregressive Moving average model as benchmark to evaluate and compare the performance of four different artificial neural network models namely, uni-variate Long-Short Term Memory (LSTM), uni-variate hybrid one dimensional convolutional neural network with LSTM (1D-CNN-LSTM), multivariate Long-short term memory and multivariate hybrid 1D-CNN-LSTM architectures. The performance evaluation is delivered through the statistical comparison of the metrics, RMSE, MAE and MAPE, and the final selection of the outperforming model is supported using Diebold-Mariano statistic test. Experiments consists of applying different types of pre-processing to the wind speed dataset and the modification of models' architecture to include either a batch normalization or a drop-out regularization layer are realized to aid in the selection of the most suitable model engineering. Results suggest the uni-variate hybrid 1D-CNN-LSTM is able to deliver short-term prediction for longer timescales while maintaining a suitable degree of accuracy.

show abstract

“…However, since RNNs are prone to problems such as gradient explosion and gradient dispersion [12], Bae et al [13] introduced a new prognostics framework named PHT, using LSTM structures as its kernel to predict the health state compatible to the sensory data, which can precisely determine the degradation point and accomplish the RUL prediction tasks. Xiao et al [14] proposed a Bi-LSTM structure fused with a 1D-CNN convolutional neural network to better fit the degradation trend obtained from rolling bearing frequency domain data.…”

Section: Introductionmentioning

confidence: 99%

ConTriFormer: triggers-guided contextual informer for remaining useful life prediction of rolling bearings

Pang

Hua

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

Rolling bearings are critical components in many industrial fields, and their stability directly affects the performance and safety of the industrial equipment. Accurate prediction of remaining useful life of rolling bearings is a heated topic in modern research. Traditional strategies are unable to efficiently exploit the significant features of the data, resulting in the inability to determine the starting time of prediction along with a reduced prediction accuracy. Accordingly, this paper proposes a novel data-driven prediction model named ConTriFormer, which incorporates multi-feature triggers focusing on various scales of input signals, and the ConvNeXt V2 sparse convolution strategy within the contextual Informer architecture for estimating remaining useful life. Firstly, significant feature indicators of the original data are calculated to construct feature triggers, resulting in a multi-feature fusion. Secondly, the starting time for prediction is obtained through quantified results from fault-sensitive triggers. Thirdly, the original signal with triggers embedded is encoded and organized into sparse matrices to facilitate the simplification of subsequent computations. Sparse features and dynamic context information reflecting bearing state changes are obtained through ConvNeXt V2 sparse convolution, which is input into the Informer structure with contextual attentive structures inside for better adaptability to long time-span dynamic data and lower spatiotemporal complexity for feature mining and prediction. Finally, the prediction results are obtained by mapping output values to the remaining life through a fully connected layer. The proposed algorithm is compared with mainstream deep learning algorithms such as Bi-LSTM and Convolutional Transformer using the XJTU-SY dataset and PHM 2012 dataset, and the effectiveness of model is verified with ablation study. Results show that, the proposed method can more accurately predict remaining useful life, providing a high-precision and intelligent method for prognostics health management of rolling bearings.

show abstract

RETRACTED: Remaining useful life prediction of wind turbine generator based on 1D-CNN and Bi-LSTM

Cited by 8 publications

References 21 publications

Review of Wind Power Bearing Wear Analysis and Intelligent Lubrication Method Research

Review of Wind Power Bearing Wear Analysis and Intelligent Lubrication Method Research

Performance comparison of probabilistic and artificial neural network models for long-sequence generation of wind speed forecasts

ConTriFormer: triggers-guided contextual informer for remaining useful life prediction of rolling bearings

Contact Info

Product

Resources

About