Vibration Signal Processing and Weak Information Extraction Method for High-Speed Shaft Fault of Wind Turbine in Wind Shear

Author(s):  
Xiaoli Xu ◽  
Xiuli Liu

Due to the influence of random wind shear in the atmospheric phenomenon, the random vibration of the main shaft of the wind turbine generator is generated. This vibration signal will be mixed with the misalignment signal of the high-speed shaft, which will cause interference to the fault diagnosis. Based on the analysis of the phenomenon of wind shear and the fault, the independent component analysis was carried out on the high-speed shaft mixed vibration signals on the basis of using rapid fixed point algorithm based on kurtosis, and the weak fault information is extracted successfully. At the same time, this method was compared with the weak information extraction method based on wavelet denoising, which proved the superiority of the proposed method. The experimental results show that the method has good field applicability and has a good application prospect in the field of weak information extraction for rotating machinery of wind power generation.

Algorithms ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 184 ◽  
Author(s):  
Qing Li ◽  
Steven Liang

Aimed at the issue of estimating the fault component from a noisy observation, a novel detection approach based on augmented Huber non-convex penalty regularization (AHNPR) is proposed. The core objectives of the proposed method are that (1) it estimates non-zero singular values (i.e., fault component) accurately and (2) it maintains the convexity of the proposed objective cost function (OCF) by restricting the parameters of the non-convex regularization. Specifically, the AHNPR model is expressed as the L1-norm minus a generalized Huber function, which avoids the underestimation weakness of the L1-norm regularization. Furthermore, the convexity of the proposed OCF is proved via the non-diagonal characteristic of the matrix BTB, meanwhile, the non-zero singular values of the OCF is solved by the forward–backward splitting (FBS) algorithm. Last, the proposed method is validated by the simulated signal and vibration signals of tapered bearing. The results demonstrate that the proposed approach can identify weak fault information from the raw vibration signal under severe background noise, that the non-convex penalty regularization can induce sparsity of the singular values more effectively than the typical convex penalty (e.g., L1-norm fused lasso optimization (LFLO) method), and that the issue of underestimating sparse coefficients can be improved.


2013 ◽  
Vol 644 ◽  
pp. 346-349
Author(s):  
Chang Zheng Chen ◽  
Yu Zhang ◽  
Quan Gu ◽  
Yan Ling Gu

It is difficult to obtain the obvious fault features of wind turbine, because the vibration signal of them are non-linear and non-stationary. To solve the problem, a multifractal analysis based on wavelet is presented in this research. The real signals of 1.5 MW wind turbine are studied by multifractal theory. The incipient fault features are extracted from the original signal. Using the Wavelet Transform Modulo Maxima Method, the multifractal was obtained. The results show that fault features of high rotational frequency of wind turbine are different from low rotational frequency, and the complexity of the vibration signals increases with the rotational frequency. These demonstrate the multifractal analysis is effective to extract the fault features of wind turbine generator.


2013 ◽  
Vol 281 ◽  
pp. 10-13 ◽  
Author(s):  
Xian You Zhong ◽  
Liang Cai Zeng ◽  
Chun Hua Zhao ◽  
Xian Ming Liu ◽  
Shi Jun Chen

Wind turbine gearbox is subjected to different sorts of failures, which lead to the increasement of the cost. A approach to fault diagnosis of wind turbine gearbox based on empirical mode decomposition (EMD) and teager kaiser energy operator (TKEO) is presented. Firstly, the original vibration signal is decomposed into a number of intrinsic mode functions (IMFs) using EMD. Then the IMF containing fault information is analyzed with TKEO, The experimental results show that EMD and TKEO can be used to effectively diagnose faults of wind turbine gearbox.


2017 ◽  
Vol 139 (3) ◽  
Author(s):  
Zhiyong Ma ◽  
Yibing Liu ◽  
Dameng Wang ◽  
Wei Teng ◽  
Andrew Kusiak

Bearing faults occur frequently in wind turbines, thus resulting in an unplanned downtime and economic loss. Vibration signal collected from a failing bearing exhibits modulation phenomenon and “cyclostationarity.” In this paper, the cyclostationary analysis is utilized to the vibration signal from the drive-end of the wind turbine generator. Fault features of the inner and outer race become visible in the frequency–cyclic frequency plane. Such fault signatures can not be produced by the traditional demodulation methods. Analysis results demonstrate effectiveness of the cyclostatonary analysis. The disassembled faulty bearing visualizes the fault.


2014 ◽  
Vol 902 ◽  
pp. 370-377
Author(s):  
Guo Xin Wu ◽  
Yun Bo Zuo ◽  
Yan Hui Shi

Aiming at the safe operation of the wind turbine, a feature extraction method of vibration signal based on the principle of blind source separation was proposed. Blind source and the current state of fault signal was separated and predicated by Using historical data of wind turbine vibration signal as the observation noise, and then fault diagnosis signal mechanical operation was analyzed, the HMM/SVM series fault diagnosis models structure was proposed. By calculating the matching degree of unknown signal and wind power equipment in the state using HMM, the features for SVM was formed to achieve the finally discriminant. The experimental results showed that the fault diagnosis method can precisely and effectively complete the wind power equipment, higher than pure HMM or SVM diagnostic accuracy.


2014 ◽  
Vol 8 (1) ◽  
pp. 716-720
Author(s):  
Xianjin Luo ◽  
Xiumei Huang

In view of failure characteristics of wind turbine gear box, this paper put forward a method for fault diagnosis based on the ensemble local means decomposition (ELMD) and fuzzy C-means clustering (FCM) method. By resolving the vibration signal of different fault state of high speed gear box by ELMD, the PF component was obtained with its singular value, which was composed of known sample followed by a test sample as the feature vector. The known sample was clustered by using the FCM clustering, and the test sample was recognized and classified . The experimental results show that the method for fault diagnosis based on ELMD and FCM clustering has good diagnosis results.


2021 ◽  
pp. 0309524X2110463
Author(s):  
Jin Xu ◽  
Xian Ding ◽  
Jiuhua Wang ◽  
Junjie Zheng

Bearings are the critical parts that support the rotating of rotor of wind turbine generators. Due to high speed revolution and affected by potential misalignment between rotor and the high speed shaft in wind turbine gearbox, the fault ratio in wind turbine generator bearings is high. Once the bearings fail, it will cause gap eccentricity, even rub, or sweeping chamber between rotor and stator. Under fault conditions, the vibration signals from rotating machinery exhibits distinct second cyclostationarity. In the light of this, the fast spectral correlation based method is applied to the fault extraction of bearings in wind turbine generators. Through converting conventional correlation into summation algorithm, the computational cost is reduced largely, meanwhile, the diagnosis accuracy is guaranteed. The effectiveness of the method in this paper is verified by two fault cases from on-site wind turbines.


2013 ◽  
Vol 819 ◽  
pp. 292-296 ◽  
Author(s):  
Dai Yi Mo ◽  
Ling Li Cui ◽  
Jin Wang ◽  
Yong Gang Xu

In order to extract the early weak fault information submerged in strong background noise of the bearing vibration signal, a delayed correlation envelope technique based on sparse signal decomposition method is proposed. This method can improve the signal to noise and extract the fault information efficiently. For the strong noise problem in the early fault, based on D-value of adjacent residual energy as the termination condition of the iterative method, reducing the noise effectively, and combine it with delayed correlation to enhance the denoising effect. The analysis results of roller bearing experimental data confirm the feasibility and validity of this method.


2017 ◽  
Vol 22 (2) ◽  
pp. 77
Author(s):  
Salome Gonzáles Chávez ◽  
William Urcuhuaranga Jesús

En este trabajo se realiza el diseño y pruebas de un nuevo dispositivo mecánico acoplado a la Cola-Veleta de un pequeño aerogenerador, tal que autorregula y controla la potencia instantánea de generación durante las sobrevelocidades de viento. La faja costera rural y zonas altoandinas del Perú, poseen gran potencial eólico, pero contrariamente se tienen nulos servicios eléctricos. En este escenario, la dotación de electricidad mediante microaerogeneradores constituye una alternativa estratégica. Una desventaja de los vientos característicos de las zonas de influencia es su alta variación de velocidades, cuyo efecto adverso en el aerogenerador es la destrucción prematura del bobinado del generador de imanes permanentes y rotura de palas. Las pruebas de regulación y control de potencia de aerogeneración se realizaron en el Túnel de Viento del Laboratorio de Energía de la Facultad de Ingeniería Mecánica de la UNI. Los resultados se dan en las curvas de potencia del conjunto aerogenerador de ensayo, donde se demuestra el control del crecimiento de la potencia generada, en el rango de velocidades de viento superiores a la condición nominal de diseño del aerogenerador. También se demuestra la confiabilidad del mecanismo cuando se somete a velocidades extremas de viento hasta alcanzar el bloqueo del aerogenerador, así como la inmediata recuperación continua de operación a medida que la velocidad de viento disminuye hacia condiciones nominales de trabajo. Palabras clave.- Control de potencia, Pequeño aerogenerador, Sobrevelocidad de viento, Dispositivo pivote de cola, Confiabilidad. ABSTRACTIn this work we make the design and testing of a new mechanical element coupled to the vane of a small wind turbine, such that self-regulates and controls the instantaneous power generating wind during overspeed. The rural coast an andean highlands of Peru have high wind potential and on the contrary zero electrical services, in this scenario the provision of electricity by small wind turbines is a strategy solution. One disadvantage of the winds from these areas is its high speed variation, whose adverse effect on the turbine generator is the premature destruction magnet generator winding and turbine blades breakage. Tests were performed on the Wind Tunnel Energy Laboratory- Mechanical Engineering Faculty, Universidad Nacional de Ingeniería-Perú. The results are given in the power curves of the wind turbine prototype. We also demonstrated the reliability of the mechanism when submitting to extreme wind speeds up to the blocking of the turbine generator, as well as the immediate recovery operation when the wind speed returns to nominal turbine generator working conditions. Keywords.- Power control, Small wind turbine, Wind overspeed, Tail pivot mechanism, Reliability.


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