Dynamic Analysis of Wind Turbine Gearbox Components

Zhao, Mingming; Ji, Jinchen

doi:10.3390/en9020110

Cited by 34 publications

(20 citation statements)

References 28 publications

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“…Due to the max radial load of bearing is known to be F r = 14 kN, the dynamic friction coefficients f 1 and f 2 of S 1 and S 2, respectively, can be determined using the geometry and material parameters [29][30][31] to f 1 = 0.02 and f 2 = 0.016, respectively. Generally, the contact damping c of the bearing in running state is variable, while the damping coefficient λ is near constant [32][33][34] and is suggested to λ = 0.02 [35]. To set the contact stiffness, a normal contact stiffness factor FKN is employed in ANSYS to estimate the contact stiffness based on the material properties and the elemental deformations.…”

Section: Construction and Updating Of Bearing Fem Modelmentioning

confidence: 99%

A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine

Liu

Huang

Xiang

2020

Sensors

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Classification of faults in mechanical components using machine learning is a hot topic in the field of science and engineering. Generally, every real-world running mechanical system exhibits personalized vibration behaviors that can be measured with acceleration sensors. However, faulty samples of such systems are difficult to obtain. Therefore, machine learning methods, such as support vector machine (SVM), neural network (NNs), etc., fail to obtain agreeable fault detection results through smart sensors. A personalized diagnosis fault method is proposed to activate the smart sensor networks using finite element method (FEM) simulations. The method includes three steps. Firstly, the cosine similarity updated FEM models with faults are constructed to obtain simulation signals (fault samples). Secondly, every simulation signal is separated into sub-signals to solve the time-domain indexes to generate the faulty training samples. Finally, the measured signals of unknown samples (testing samples) are inserted into the trained SVM to classify faults. The personalized diagnosis method is applied to detect bearing faults of a public bearing dataset. The classification accuracy ratios of six types of faults are 90% and 92.5%, 87.5% and 87.5%, 85%, and 82.5%, respectively. It confirms that the present personalized diagnosis method is effectiveness to detect faults in the absence of fault samples.

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Section: Construction and Updating Of Bearing Fem Modelmentioning

confidence: 99%

A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine

Liu

Huang

Xiang

2020

Sensors

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“…Jiang et al [53] also performed multibody simulation of the same drivetrain and focused on the dynamic loads on the planetary bearings. Zhao and Ji [123] proposed a four-degree-of-freedom dynamic model and studied the dynamic responses of both the gears and bearings of a wind turbine gearbox. Wei et al [124] derived a torsional vibration model for the dynamic response analysis of a gearbox transmission system considering the influence of uncertain parameters.…”

Section: Mechanical and Electrical Componentsmentioning

confidence: 99%

Structural Reliability Analysis of Wind Turbines: A Review

Jiang

Dong

et al. 2017

Energies

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Abstract:The paper presents a detailed review of the state-of-the-art research activities on structural reliability analysis of wind turbines between the 1990s and 2017. We describe the reliability methods including the first-and second-order reliability methods and the simulation reliability methods and show the procedure for and application areas of structural reliability analysis of wind turbines. Further, we critically review the various structural reliability studies on rotor blades, bottom-fixed support structures, floating systems and mechanical and electrical components. Finally, future applications of structural reliability methods to wind turbine designs are discussed.

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“…Depending on the number of degrees of freedom, the rigid body dynamic models can be divided into three main groups: (i) purely torsional model, (ii) torsional -transverse model and (iii) three dimensional model [1]. The purely torsional model has one degree of freedom per node and can be used for the analyses of spur planetary gearboxes if transverse, tilting and axial motions, and gyroscopic effects are negligible [11,13,17,20,31,32]. Expanding the purely torsional model to include transverse motions leads to the three degrees of freedom per node torsional -transverse model [6,9,33,34].…”

Section: Introductionmentioning

confidence: 99%

Dynamic behaviour of three-dimensional planetary geared rotor systems

Tatar

Schwingshackl

Friswell

2019

Mechanism and Machine Theory

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A six degrees of freedom dynamic model of a planetary geared rotor system with equally spaced planets is developed by considering gyroscopic effects. The dynamic model is created using a lumped parameter model of the planetary gearbox and a finite element model of the rotating shafts using Timoshenko beams. The gears and carrier in the planetary gearbox are assumed to be rigid, and the gear teeth contacts and bearing elements are assumed to be flexible. The modal analysis results show that torsional and axial vibrations on the shafts are coupled in the helical gearing configuration due to the gear helix angle whereas these vibrations become uncoupled for spur gearing. Mainly, the vibration modes are classified as coupled torsional-axial, lateral and gearbox for the helical gear configuration, and torsional, axial, lateral and gearbox for the spur gear configuration. Modal energy analysis is used to quantify the coupling level between the shafts and the planetary gearbox, highlighting the impact of the gearbox on certain mode families. Gyroscopic effects of the planetary gearbox are found to be of great importance in the gearbox dominated modes.

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Dynamic Analysis of Wind Turbine Gearbox Components

Cited by 34 publications

References 28 publications

A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine

A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine

Structural Reliability Analysis of Wind Turbines: A Review

Dynamic behaviour of three-dimensional planetary geared rotor systems

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