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 310 publications

(150 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, 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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“…To determine the real-time operation status of wind turbines and to implement reasonable maintenance of wind turbines, modern large wind turbines are equipped with SCADA (Supervisory Control and Data Acquisition) systems. The application of SCADA data for wind turbine state identification, fault diagnosis, and early warning is currently a hot research topic in academic circles [2][3][4] and focuses mainly on statistical methods or data mining methods, such as neural networks and support vector machines [5,6]. In terms of statistical methods, Kusiak and others identified faulty wind turbines through comparative analysis of SCADA data of more than three wind turbines [7,8].…”

Section: Introductionmentioning

confidence: 99%

Calculation and Analysis of Wind Turbine Health Monitoring Indicators Based on the Relationships with SCADA Data

et al. 2020

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This paper proposes an evaluation index of wind turbine generator operating health based on the relationships with SCADA (Supervisory Control and Data Acquisition) data. First, the relationship among the data from a wind turbine SCADA system is thoroughly analyzed. Then, a time based sliding window model is used to process the SCADA data by the bin method, and a running state model of the wind turbine is established by data fitting. Taking the normal operation state model of the wind turbine as the standard reference and based on the Euclidean distance between the state model curve and the standard model curve, the health index of the wind turbine operation state is proposed. Finally, using SCADA data from two 2 MW direct-drive wind turbines as examples for analysis and discussion, the results show that: (1) health indicators have good stability and sensitivity to wind turbine operating conditions; (2) the width of the data window in the sliding window model must cover all operating conditions of the wind turbine to ensure that the health index depicts the operating state of the wind turbine; (3) the data window width, window increment, and data fitting modeling all affect the health indicators, and thus, the selection of the sliding window model parameters and the data relationship modeling methods should consider the accuracy and real-time performance of the health indicators; and (4) the data acquisition cycle does not affect the health indicators. Once the basic characteristics of the data relations are known, direct data fitting modeling is more efficient than bin preprocessing modeling.

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

Cited by 310 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

Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model

Calculation and Analysis of Wind Turbine Health Monitoring Indicators Based on the Relationships with SCADA Data

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