Wind turbine planetary gearbox health diagnostic using time-varying meshing stiffness

Abouel-Seoud, Shawki; Dyab, Eid S.

doi:10.7158/m12-035.2014.12.1

Cited by 3 publications

(3 citation statements)

References 1 publication

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“…From Figs. 3 and 4, we can seen that, the just-determined part includes 14 blocks: the 12 measurement variables are scattered in five blocks: x 19 5 . Also, only three parts b 1 , b 2 , and b 3 are strongly connected blocks, that is, they have no intersection with other blocks and are independent parts.…”

Section: Numeric Optimal Sensor Configuration Solutions For a Wind Tumentioning

confidence: 97%

“…Wind turbine gearbox failure mainly occurs in the gears and shafts, only gear fault is considered in this model. Related research demonstrate that when tooth faults such as tooth cracking, tooth flaking, or tooth broken occur, the tooth mesh stiffness decreases significantly [18][19][20][21]. More severely the fault is, more sharp the mesh stiffness will decrease.…”

Section: Wind Turbine Gearbox Vibration Model Involving Tooth Faultsmentioning

confidence: 99%

“…However, and all this cannot be observed in a vibration model. Some researchers mention that mesh stiffness, which always appears in a vibration model, can reflect gear failure [18][19][20][21]. The reduction in the mesh stiffness results in gear failures of different degrees; with the decrease in the mesh stiffness, the gear fault becomes more serious.…”

Section: Introductionmentioning

confidence: 99%

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Numeric optimal sensor configuration solutions for wind turbine gearbox based on structure analysis

Wang

Zhao

Guo

et al. 2017

IET Renewable Power Generation

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The wind turbine gearbox, an important component of wind turbines, has a high failure rate and maintenance cost; therefore, several vibration sensors are installed to execute effective condition monitoring, fault diagnosis, and prediction, for reducing the downtime caused by faults. The sensor positions should directly and accurately reflect the gearbox operating status and provide several signals to the condition monitoring system. This article studies a gearbox model and the gear tooth faults, and builds a gearbox gear vibration model involving these faults. Using structure analysis, sensor configuration solutions rendering all the tooth faults detectable and all the faults isolable are, respectively, obtained, based on the gear vibration model. The sensor configuration solutions of the wind turbine gearbox can guide wind turbine gearbox sensor installation in practice and support common wind turbine gearbox sensor configuration solutions in theory.

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Section: Numeric Optimal Sensor Configuration Solutions For a Wind Tumentioning

confidence: 97%

Section: Wind Turbine Gearbox Vibration Model Involving Tooth Faultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Numeric optimal sensor configuration solutions for wind turbine gearbox based on structure analysis

Wang

Zhao

Guo

et al. 2017

IET Renewable Power Generation

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Application of an Adaptive Neuro Fuzzy Inference System for Low Speed Planetary Gearbox Vibration Control

Watany

Eltantawie

Abouel-Seoud

2015

Journal of Low Frequency Noise, Vibration and Active Control

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The gear pair assembly remains one of the major vibration sources in power transmission systems of mechanical rotating machinery. The gear vibration signature is often dominated by several high-level tonal peaks that occur at the fundamental gear mesh and its harmonics. The primary forcing function is produced by the gear transmission error excitation resulting from tooth profile errors, misalignment and elastic deformation. The excessive dynamic response generated can frequently lead to structural fatigue failure. Therefore, it is highly desirable to control the low-speed planetary gearbox rotational vibration acceleration levels. To achieve this goal, the present study demonstrates a method by which these vibrations produced due to the primary forcing function originating from the mesh points can be controlled. Hence, to deal with both the meshing stiffness generation and vibration transmissibility processes more effectively, the control system must be applied close to the gear's connection. This close proximity will allow the application of a single controller. Selection of the proper rule base depending upon the situation can be achieved by the use of an adaptive neuro-fuzzy inference system (ANFIS) controller as an integrated approach for purposes of control to yield excellent results, and this is the highlight of this paper. The results presented in the paper show that the outputs take less time to stabilize. Moreover, due to incorporation of the ANFIS controller with the plant, it is observed that the gearbox reaches the desired vibration very quickly in relatively shorter time. Corresponding actual output of the ANFIS identifier of the m th component. Keywords INTRODUCTIONFuzzy logic controller has previously been implemented to adjust the boundarylayer width according to the speed error [1]. The drawback of their controller is that it depends on the equivalent control and on the system parameters. An excellent control scheme for control of various applications in the industrial sector was developed [2]. This controller had many advantages over conventional techniques.The Takagi-Sugeno (T-S) fuzzy model identification method by which a great numbers of systems whose parameters vary dramatically with working states can be identified via fuzzy neural networks (FNNs). The suggested method could overcome the drawbacks of traditional linear system identification methods, which are only effective under certain narrow working states, and provide global dynamic description based on which further control of such systems may be carried out. An application of the neuro-fuzzy logic pulse-width modulation (PWM) based proportional derivative (PD) controller was proposed, where a comparison with the conventional techniques (such as fuzzy-proportional integral derivative (PID) and fuzzy-proportional derivative (PD) controllers) was done [3]. MATLAB Simulink software was employed to show that the fuzzy logic based PD controller provides robust control for non-linear power electronics variable switching structure ...

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Dynamic modeling of defective gears

Bloju

Korka

2021

SUBB Eng

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The use of geared transmissions has a long history and a rich experience, which has allowed the development of an intense research activity that has led to modern design methods, mostly standardized and execution technologies that have become traditional. As the fundamental sciences have provided more and more in-depth and refined knowledge, namely performance algorithms of optimal synthesis, the design in the field of gear transmissions has evolved by integrating in the calculation methods a growing number of elements. of influence (materials, geometry, dimensional and shape deviations, heat treatments, kinematic, energetic, dynamic factors, etc.). Automated modeling and simulation currently allow the prediction of behavior - from all points of view of a transmission - during operation.

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Wind turbine planetary gearbox health diagnostic using time-varying meshing stiffness

Cited by 3 publications

References 1 publication

Numeric optimal sensor configuration solutions for wind turbine gearbox based on structure analysis

Numeric optimal sensor configuration solutions for wind turbine gearbox based on structure analysis

Application of an Adaptive Neuro Fuzzy Inference System for Low Speed Planetary Gearbox Vibration Control

Dynamic modeling of defective gears

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