A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings

Liu, Zepeng; Zhang, Long

doi:10.1016/j.measurement.2019.107002

Cited by 370 publications

(189 citation statements)

References 106 publications

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“…Lead‐based piezoelectric ceramics, mainly Pb(Zr,Ti)O 3 (PZT), have been widely applied in various electronic devices due to their low‐cost processing and excellent piezoelectric properties . Nevertheless, lead‐free piezoelectric ceramics, including (K,Na)NbO 3 (KNN), BaTiO 3 (BT), and Na 0.5 Bi 0.5 TiO 3 (NBT)‐based materials, are fast becoming a key research topic in replacing conventional lead‐based materials due to serious concerns of highly toxic Pb/PbO‐based compound to human beings and environment . Among them, electroceramics with NBT‐based ceramics, for example, Na 0.5 Bi 0.5 TiO 3 ‐SrTiO 3 (NBT‐ST), Na 0.5 Bi 0.5 TiO 3 ‐K 0.5 Bi 0.5 TiO 3 (NBT‐KBT), Na 0.5 Bi 0.5 TiO 3 ‐BaTiO 3 (NBT‐BT), and Na 0.5 Bi 0.5 TiO 3 ‐Ba (Ti 0.95 Zr 0.05 )O 3 , are known as the most potential lead‐free candidate because of the excellent electrostrain .…”

Section: Introductionmentioning

confidence: 99%

“…[3][4][5][6][7] Nevertheless, lead-free piezoelectric ceramics, including (K,Na)NbO 3 (KNN), 8 BaTiO 3 (BT), 9 and Na 0.5 Bi 0.5 TiO 3 (NBT)-based materials, [10][11][12][13] are fast becoming a key research topic in replacing conventional leadbased materials due to serious concerns of highly toxic Pb/PbO-based compound to human beings and environment. [14][15][16][17][18] Among them, electroceramics with NBT-based ceramics, for example, Na 0.5 Bi 0.5 TiO 3 -SrTiO 3 (NBT-ST), Na 0.5 Bi 0.5 TiO 3 -K 0.5 Bi 0.5 TiO 3 (NBT-KBT), Na 0.5 Bi 0.5 TiO 3 -BaTiO 3 (NBT-BT), and Na 0.5 Bi 0.5 TiO 3 -Ba (Ti 0.95 Zr 0.05 ) O 3 , 19,20 are known as the most potential lead-free candidate because of the excellent electrostrain. [15][16][17][18] Multilayer actuators (MLAs) are widely applied in practical piezoelectric applications with co-fired ceramic and inner electrodes.…”

Section: Introductionmentioning

confidence: 99%

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Large electrostrain in low‐temperature sintered NBT‐BT‐0.025FN incipient piezoceramics

Zhang

Marwat

et al. 2020

J Am Ceram Soc

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It is well‐known that the high sintering temperatures of (Na1/2Bi1/2)TiO3 (NBT)‐based incipient piezoceramics limit their applications on low‐cost multilayer piezoelectric actuators with base metal internal electrodes. In this work, a synergistic sintering additive of 0.75 wt% Li2CO3 and 0.75 wt% CuO was utilized to explore the effect on sintering behavior and electrostrain of (Na1/2Bi1/2)0.935Ba0.065Ti0.975(Fe1/2Nb1/2)0.025O3 (NBT‐BT‐0.025FN) incipient piezoceramic. The optimal sintering temperature of NBT‐BT‐0.025FN decreased from 1160°C to 940°C due to the formation of a liquid phase, with limited degradation on electrostrain. When sintered at 940°C, a large strain of 0.39% (a nominal piezocoefficient d33∗ of 571 pm/V) was achieved, which is promising for potential actuator applications co‐firing with Ag electrodes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Large electrostrain in low‐temperature sintered NBT‐BT‐0.025FN incipient piezoceramics

Zhang

Marwat

et al. 2020

J Am Ceram Soc

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“…At present, many achievements have been made in the research on fault prediction and diagnosis of wind turbine [10], [11]. The research on fault diagnosis of wind turbine based on condition monitoring system (CMS) is relatively mature [12].…”

Section: Introductionmentioning

confidence: 99%

Research on Fault Diagnosis of Wind Turbine Based on SCADA Data

Liu

Wang

2020

IEEE Access

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Effective early warning of wind turbine failures is of great significance to reduce the operation and maintenance costs of wind farms and improve power generation efficiency. At present, most wind farms are installed with supervisory control and data acquisition (SCADA) system, and SCADA data contains a lot of hidden information, which can be used for fault early warning. This paper uses the generator temperature and gearbox oil temperature in the SCADA data as the entry point for fault warning. Firstly, the eXtreme gradient boosting (XGBoost) algorithm is used to establish the normal temperature regression prediction model of wind turbine components. Then, the residual between the predicted value and the actual value is calculated, and the change trend of the residual is monitored by the principle of exponentially weighted moving-average (EWMA) control chart. Finally, by setting an appropriate threshold, the variation trend of the residual is judged to determine the occurrence and development of the fault. This paper uses two fault detection methods: fixed threshold and dynamic threshold based on adaptive algorithm, and compares the advantages and disadvantages of the two methods. Based on the SCADA data of a wind farm in Inner Mongolia (China), this paper designs the fault early warning test of the wind turbine generator and gearbox. The experimental results show that for the generator, the fixed fault threshold method can give the fault alarm 3 hours in advance, while the dynamic fault threshold determination method can give fault alarm 4.25 hours in advance. For gearbox, the fixed fault threshold method can give the fault alarm 2 hours in advance, while the dynamic threshold fault diagnosis method can send out the fault alarm 2.75 hours in advance. INDEX TERMS Wind turbine; Fault warning; eXtreme gradient boosting (XGBoost); exponentially weighted moving-average (EWMA); supervisory control and data acquisition (SCADA)

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“…At present, doubly-fed wind turbines (DFWT) have become the main units for large-capacity wind farms [4]. Due to installation errors, deformation after loading or frequent wind speed fluctuations, misalignment between the gearbox and the generator often happens [5].…”

Section: Introductionmentioning

confidence: 99%

Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model

Xiao

Hua

2020

Algorithms

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Due to the harsh working environment of wind turbines, various types of faults are prone to occur during long-term operation. Misalignment faults between the gearbox and the generator are one of the latent common faults for doubly-fed wind turbines. Compared with other faults like gears and bearings, the prediction research of misalignment faults for wind turbines is relatively few. How to accurately predict its developing trend has always been a difficulty. In this paper, a combined forecasting model is proposed for misalignment fault prediction of wind turbines based on vibration and current signals. In the modelling, the improved Multivariate Grey Model (IMGM) is used to predict the deterministic trend and the Least Squares Support Vector Machine (LSSVM) optimized by quantum genetic algorithm (QGA) is adopted to predict the stochastic trend of the fault index separately, and another LSSVM optimized by QGA is used as a non-linear combiner. Multiple information of time-domain, frequency-domain and time-frequency domain of the wind turbine's vibration or current signals are extracted as the input vectors of the combined forecasting model and the kurtosis index is regarded as the output. The simulation results show that the proposed combined model has higher prediction accuracy than the single forecasting models.Algorithms 2020, 13, 56 For the complex non-linear system, a single forecasting model is not enough to obtain ideal prediction results. Therefore, in order to predict the mechanical fault accurately, the combined forecasting model has attracted more and more attention from scholars. For example, in Ref.[18], the improved Grey Model (GM (1,1)) and the Back Propagation (BP) neural network optimized by Genetic Algorithm (GA) were used as the single forecasting models. The minimum sum of error squares was used as the combination principle to assign appropriate weight coefficients to them. The combined forecasting model had a smaller prediction error. Ref. [19] proposed a calculation method of combined weight coefficients for the unequal weight of error. The combined forecasting model was constructed by Multivariate Grey Model (MGM (1, n)) and Extreme Learning Machine (ELM) neural network. The combined forecasting model was more suitable for predicting the trend of the bearing fault. In Ref.[20], according to the minimum variance principle, Support Vector Machine (SVM) and grey model were combined to make up the shortcomings of single forecasting models. In Ref. [21], SVM was used as the combiner of forecasting models. The Kalman filter, BP neural network and SVM model were used as single forecasting models. The prediction errors of the single forecasting models were larger than that of the combined model. In Ref. [22], the BP neural network was used to determine the weight coefficients of each single forecasting model. The combined forecasting model using GM (1,1,θ) optimized by Particle Swarm Optimization (PSO) algorithm and SVM optimized by PSO achieved better prediction accuracy for the short-term load of...

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A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings

Cited by 370 publications

References 106 publications

Large electrostrain in low‐temperature sintered NBT‐BT‐0.025FN incipient piezoceramics

Large electrostrain in low‐temperature sintered NBT‐BT‐0.025FN incipient piezoceramics

Research on Fault Diagnosis of Wind Turbine Based on SCADA Data

Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model

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