Vibration analysis for large-scale wind turbine blade bearing fault detection with an empirical wavelet thresholding method

Liu, Zepeng; Zhang, Long; Carrasco, Joaquín

doi:10.1016/j.renene.2019.06.094

Cited by 100 publications

(44 citation statements)

References 28 publications

(31 reference statements)

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“…MGM (1,11) 0.6378 0.7632 Barely qualified IMGM (1,11) 0.4002 0.8857 Qualified In Table 10, MGM (1,11) is barely qualified grade and IMGM (1,11) belongs to the qualified grade. Therefore, IMGM (1,11) is more suitable for predicting the kurtosis of the current signal than MGM (1,11). The prediction results of MGM (1,11) and IMGM (1,11) of the current signal are shown in Figure 6.…”

Section: P Precision Gradementioning

confidence: 99%

“…The prediction results of MGM (1,11) and IMGM (1,11) of the current signal are shown in Figure 6. By comparing the prediction results in Figure 6a and Figure 6b, the predicted fluctuation range of IMGM (1,11) is obviously smaller than that of MGM (1,11). It is because when predicting the last 15 points, the data used to establish the MGM (1,11) has always been the first 45 points, and the data has not been updated with the increase prediction steps.…”

Section: P Precision Gradementioning

confidence: 99%

“…There are many commonly used signals for mechanical fault prediction, such as vibration signals, current signals, temperature signals, pressure signals, etc. [9][10][11][12][13]. When the equipment fails, the 2 of 20 amplitude of the mechanical vibration will increase [14].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: P Precision Gradementioning

confidence: 99%

Section: P Precision Gradementioning

confidence: 99%

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Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model

Xiao

Hua

2020

Algorithms

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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 .…”

Section: Introductionmentioning

confidence: 99%

“…Lead-based piezoelectric ceramics, mainly Pb(Zr,Ti)O 3 (PZT), [1][2][3] have been widely applied in various electronic devices due to their low-cost processing and excellent piezoelectric properties. [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.…”

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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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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Descriptive Study of a Rotary Machine Affected by Misalignment and Imbalance Applying the Wavelet Transform

Sandoval-Rodríguez

Romero

Lengerke

et al. 2021

Lecture Notes in Electrical Engineering

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Vibration analysis for large-scale wind turbine blade bearing fault detection with an empirical wavelet thresholding method

Cited by 100 publications

References 28 publications

Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model

Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model

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

Descriptive Study of a Rotary Machine Affected by Misalignment and Imbalance Applying the Wavelet Transform

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