Improved Remaining Useful Life Estimation of Wind Turbine Drivetrain Bearings Under Varying Operating Conditions

Rezamand, Milad; Kordestani, Mojtaba; Orchard, Marcos E.; Carriveau, Rupp; Ting, David S.‐K.; Saif, Mehrdad

doi:10.1109/tii.2020.2993074

Cited by 49 publications

(22 citation statements)

References 29 publications

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“…The researches on the related technology of the wind turbine bearings are receiving more and more attention. At present, most researches are aimed at judging the working state of wind turbine bearings for reasonable operation and maintenance arrangement, such as condition monitoring, 4,5 fault extraction, [6][7][8] fault diagnosis, [9][10][11] fault prediction, 12 life prediction, 13 health assessment, 14 temperature prediction, 15 etc. The research on 1 School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang, China mechanics analysis of wind turbine bearings mainly focuses on the pitch bearing or the yaw bearing.…”

Section: Introductionmentioning

confidence: 99%

Internal load distribution of single-row tapered roller bearings doubly supporting main shaft of wind turbine

Gao²

2022

Advances in Mechanical Engineering

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In order to accurately understand the internal loading state of the single-row tapered roller bearings doubly supporting the main shaft of wind turbine, a 5-DOF mechanical model of such bearing combination in wind turbine was established which considered the axial preload, bearing ring tilt, and roller crown. The positions of the points on the bearing raceways were described mathematically in the Cartesian coordinate system, and the mathematical relationship between the roller-raceway contact deformations and the bearing ring displacements was derived. The effect of bearing ring tilt caused by external loads on the contact load distributions along rollers was considered by dividing the rollers into laminae. The contact stress distribution between roller and raceway was obtained by iterative calculation of the finite length line contact model. The RMSE of the roller load distributions of the upwind bearing and the downwind bearing is 2.25 kN and 3.02 kN respectively. The obtained smallest size necessary for the roller laminae is 1.25% of the roller length. The bearing ring tilt will cause uneven contact load distribution along the roller, and modification of the roller profile improves the load and stress distribution. The machining error leads to the unbalanced contact stress distribution along the roller.

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Section: Introductionmentioning

confidence: 99%

Internal load distribution of single-row tapered roller bearings doubly supporting main shaft of wind turbine

Gao²

2022

Advances in Mechanical Engineering

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“…Taking the data of LJ2000 series fighter data as samples, the hybrid prediction method was evaluated with relative accuracy, and it was found that the prediction effect of distributed structure was better [ 27 ]. Rezamand et al constructed a hybrid prediction method based on real-time Supervisory Control and Data Acquisition (SCADA) and vibration signals to predict the Remaining Useful Life (RUL) of wind turbine bearings, made an empirical analysis of the hybrid model, and concluded that the prediction accuracy of this method was higher than that of the Bayesian algorithm [ 28 ]. Chen, Zhang, and Vachtsevanos proposed a prediction method of machine health condition based on Neural-Fuzzy Systems (NFSs) and Bayesian algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Regional NO₂ Concentration Prediction Using Discrete Wavelet Transform and an LSTM Network

Liu

Zhang

Wang

et al. 2021

Computational Intelligence and Neuroscience

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Achieving accurate predictions of urban NO2 concentration is essential for effectively control of air pollution. This paper selected the concentration of NO2 in Tianjin as the research object, concentrating predicting model based on Discrete Wavelet Transform and Long- and Short-Term Memory network (DWT-LSTM) for predicting daily average NO2 concentration. Five major atmospheric pollutants, key meteorological data, and historical data were selected as the input indexes, realizing the effective prediction of NO2 concentration in the next day. Firstly, the input data were decomposed by Discrete Wavelet Transform to increase the data dimension. Furthermore, the LSTM network model was used to learn the features of the decomposed data. Ultimately, Support Vector Regression (SVR), Gated Regression Unit (GRU), and single LSTM model were selected as comparison models, and each performance was evaluated by the Mean Absolute Percentage Error (MAPE). The results show that the DWT-LSTM model constructed in this paper can improve the accuracy and generalization ability of data mining by decomposing the input data into multiple components. Compared with the other three methods, the model structure is more suitable for predicting NO2 concentration in Tianjin.

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“…Kolesov et al (2017) propose a fault diagnosis of complex systems under uncertainty, but this method only solves the problem of uncertain parameters and fails to consider the dynamic correlation in the complex systems. Avendaño-Valencia and Fassois (2017) and Rezamand et al (2020) present the useful life estimation and health monitoring methods of wind turbine under various uncertain operating conditions. The validity and efficiency of this method are proved by verification and comparison.…”

Section: Introductionmentioning

confidence: 99%

Optimal fault diagnosis strategy for complex systems considering common cause failure under epistemic uncertainty

Duan

Huang

2021

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Purpose This paper aims to deal with the problems such as epistemic uncertainty, common cause failure (CCF) and dynamic fault behaviours that arise in complex systems and develop an effective fault diagnosis method to rapidly locate the fault when these systems fail. Design/methodology/approach First, a dynamic fault tree model is established to capture the dynamic failure behaviours and linguistic term sets are used to obtain the failure rate of components in complex systems to deal with the epistemic uncertainty. Second, a β factor model is used to construct a dynamic evidence network model to handle CCF and some parameters obtained by reliability analysis are used to build the fault diagnosis decision table. Finally, an improved Vlsekriterijumska Optimizacija I Kompromisno Resenje algorithm is developed to obtain the optimal diagnosis sequence, which can locate the fault quickly, reduce the maintenance cost and improve the diagnosis efficiency. Findings In this paper, a new optimal fault diagnosis strategy of complex systems considering CCF under epistemic uncertainty is presented based on reliability analysis. Dynamic evidence network is easy to carry out the quantitative analysis of dynamic fault tree. The proposed diagnosis algorithm can determine the optimal fault diagnosis sequence of complex systems and prove that CCF should not be ignored in fault diagnosis. Originality/value The proposed method combines the reliability theory with multiple attribute decision-making methods to improve the diagnosis efficiency.

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Improved Remaining Useful Life Estimation of Wind Turbine Drivetrain Bearings Under Varying Operating Conditions

Cited by 49 publications

References 29 publications

Internal load distribution of single-row tapered roller bearings doubly supporting main shaft of wind turbine

Internal load distribution of single-row tapered roller bearings doubly supporting main shaft of wind turbine

A Novel Method for Regional NO₂ Concentration Prediction Using Discrete Wavelet Transform and an LSTM Network

Optimal fault diagnosis strategy for complex systems considering common cause failure under epistemic uncertainty

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Improved Remaining Useful Life Estimation of Wind Turbine Drivetrain Bearings Under Varying Operating Conditions

Cited by 49 publications

References 29 publications

Internal load distribution of single-row tapered roller bearings doubly supporting main shaft of wind turbine

Internal load distribution of single-row tapered roller bearings doubly supporting main shaft of wind turbine

A Novel Method for Regional NO2 Concentration Prediction Using Discrete Wavelet Transform and an LSTM Network

Optimal fault diagnosis strategy for complex systems considering common cause failure under epistemic uncertainty

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Product

Resources

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A Novel Method for Regional NO₂ Concentration Prediction Using Discrete Wavelet Transform and an LSTM Network