Wind Turbine Planetary Gearbox Research Articles

A dual path optimization ridge estimation method for condition monitoring of planetary gearbox under varying-speed operation

Abstract Tacholess order tracking method is an effective tool for condition monitoring of planetary gearbox under varying-speed operation to reduce the measurement cost and avoid the inconvenience in the installation and adjustment. The robust ridge estimation is an indispensable procedure in the tacholess order tracking. However, conventional ridge estimation methods are difficult to extract the targeted ridge from the highly nonstationary signals. In this paper, we propose a novel method, termed dual path optimization ridge estimation (DPORE), which implants the idea of local integration to adapt with the intensively oscillated ridge. An in-situ vibration signal collected from a wind turbine planetary gearbox provided by the contest during the conference CMMNO December 2014 is employed to validate the proposed method. As a result, an important time-frequency ridge with weak energy and intricate shape is successfully extracted by the proposed method, while two popular ridge estimation methods cannot well track the complete time-frequency ridge. Moreover, the results obtained by the proposed method outperform those provided by the contestants in the conference contest. Finally, the accuracy of estimated ridge is further demonstrated by order analysis and one of the planet bearings in planetary gearbox with an inner race defect can be detected based on the order spectrum.

Application of Reassigned Wavelet Scalogram in Wind Turbine Planetary Gearbox Fault Diagnosis under Nonstationary Conditions

Wind turbine planetary gearboxes often run under nonstationary conditions due to volatile wind conditions, thus resulting in nonstationary vibration signals. Time-frequency analysis gives insight into the structure of an arbitrary nonstationary signal in joint time-frequency domain, but conventional time-frequency representations suffer from either time-frequency smearing or cross-term interferences. Reassigned wavelet scalogram has merits of fine time-frequency resolution and cross-term free nature but has very limited applications in machinery fault diagnosis. In this paper, we use reassigned wavelet scalogram to extract fault feature from wind turbine planetary gearbox vibration signals. Both experimental and in situ vibration signals are used to evaluate the effectiveness of reassigned wavelet scalogram in fault diagnosis of wind turbine planetary gearbox. For experimental evaluation, the gear characteristic instantaneous frequency curves on time-frequency plane are clearly pinpointed in both local and distributed sun gear fault cases. For in situ evaluation, the periodical impulses due to planet gear fault are also clearly identified. The results verify the feasibility and effectiveness of reassigned wavelet scalogram in planetary gearbox fault diagnosis under nonstationary conditions.

Power loss prediction: Application to a 2.5 MW wind turbine gearbox

A 2.5 MW wind turbine gearbox design was considered to perform a power loss prediction using different wind turbine gear oil formulations. A gearbox power loss model, previously validated with experimental results, was used to predict the efficiency of a full wind turbine planetary gearbox. The power loss model account the gears and rolling bearing losses using well established models calibrated with a method proposed by the author. The calculations clearly showed that significant energy savings can be achieved by selecting different base oils, modifying gear tooth geometry, or combining both.

Vibration‐based wind turbine planetary gearbox fault diagnosis using spectral averaging

AbstractPlanetary gearboxes (PGBs) are widely used in the drivetrain of wind turbines. Any PGB failure could lead to a significant breakdown or major loss of a wind turbine. Therefore, PGB fault diagnosis is very important for reducing the downtime and maintenance cost and improving the safety, reliability, and lifespan of wind turbines. The wind energy industry currently utilizes vibratory analysis as a standard method for PGB condition monitoring and fault diagnosis. Among them, the vibration separation is considered as one of the well‐established vibratory analysis techniques. However, the drawbacks of the vibration separation technique as reported in the literature include the following: potential sun gear fault diagnosis limitation, multiple sensors and large data requirement, and vulnerability to external noise. This paper presents a new method using a single vibration sensor for PGB fault diagnosis using spectral averaging. It combines the techniques of enveloping, Welch's spectral averaging, and data mining‐based fault classifiers. Using the presented approach, vibration fault features for wind turbine PGB are extracted as condition indicators for fault diagnosis and condition indicators are used as inputs to fault classifiers for PGB fault diagnosis. The method is validated on a set of seeded localized faults on all gears: sun gear, planetary gear, and ring gear. The results have shown a promising PGB fault diagnosis performance with the presented method. Copyright © 2015 John Wiley & Sons, Ltd.

A novel order tracking method for wind turbine planetary gearbox vibration analysis based on discrete spectrum correction technique

Wind turbine gearboxes generally exhibit complex vibration characteristics due to wide variations in the operating conditions, and dynamics of the structure coupled with flexible supports. Conventional spectral analysis method may not provide reliable health monitoring and fault diagnosis of the gearbox. In this study, a novel order tracking method based on discrete spectrum correction technique is proposed to analyze wind turbine gearbox vibration for the purposes of health monitoring and fault diagnosis. The effectiveness and robustness of the proposed method are demonstrated through simulations and engineering tests. The results show that the shaft rotating speed could be accurately identified from the vibration signal together with amplitudes of significant gear meshing components. Modulation sidebands of both the planetary and fixed-shaft gears in a healthy wind turbine gearbox were further analyzed, which revealed inherent shaft misalignment in the fixed-shaft gear. Meshing frequency of the planetary gear was modulated by both the rotating frequencies of sun gear and planetary carrier, while fundamental modulation frequency of the planetary carrier was found to be related to rotating frequency of the carrier multiplied by the number of planet gears. The monitoring of such particular vibration features would be helpful in enhancing the operational performance of wind turbines through reliable health monitoring of gearboxes and reducing the misdiagnosis of faults.

Time–frequency analysis based on Vold-Kalman filter and higher order energy separation for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions

Planetary gearbox fault diagnosis under nonstationary conditions is important for many engineering applications in general and for wind turbines in particular because of their time-varying operating conditions. This paper focuses on the identification of time-varying characteristic frequencies from complex nonstationary vibration signals for fault diagnosis of wind turbines under nonstationary conditions. We propose a time–frequency analysis method based on the Vold-Kalman filter and higher order energy separation (HOES) to extract fault symptoms. The Vold-Kalman filter is improved such that it is encoders/tachometers-free. It can decompose an arbitrarily complex signal into mono-components without resorting to speed inputs, thus satisfying the mono-component requirement by the HOES algorithm. The HOES is then used to accurately estimate the instantaneous frequency because of its high adaptability to local signal changes. The derived time–frequency distribution features fine resolution without cross-term interferences and thus facilitates extracting time-varying frequency components from highly complex and nonstationary signals. The method is illustrated and validated by analyzing simulated and experimental signals of a planetary gearbox in a wind turbine test rig under nonstationary running conditions. The results have shown that the method is effective in detecting both distributed (wear on every tooth) and localized (chipping on one tooth) gear faults.

Planetary Gearbox Fault Diagnosis under Time-variant Conditions Based on Iterative Generalized Synchrosqueezing Transform

The synchrosqueezing transform can effectively improve the readability of time-frequency representation of mono-component and constant frequency signals, but blurs still occur to the time-frequency representation when analyzing multi-component and time-variant frequency signals. In order to address the time-frequency blur issue, an improved synchrosqueezing transform based on iterative generalized demodulation is proposed. The signal is decomposed into mono-components of constant frequency, via iterative generalized demodulation and filtering. The instantaneous frequency and time-frequency representation of such mono-component are accurately estimated by synchrosqueezing transform. The time-frequency representation of original signal is obtained by superposing the reconstructed time-frequency representations of all the mono-components. This proposed method effectively improves the time-frequency readability of synchrosqueezing transform when analyzing time-variant frequency signals, and generalizes the synchrosqueezing transform to multi-component signals as well. Using this method, the vibration signal frequency contents and their time-varying characteristics of a wind turbine planetary gearbox under time-variant conditions are effectively identified, and the gear fault is diagnosed.

Load sharing and distributed on the gear flank of wind turbine planetary gearbox

In the design of wind turbine gearboxes, the most important objective is to improve the durability to guarantee a service life of more than 20 years. Planetary gearsets, commonly used in wind turbine gearboxes, should have good load distribution and load sharing among gears to improve the durability. This work investigates how the optimal helix modification influence both the load distribution over the gear tooth flank and the planet load sharing. A whole system model is developed to analyze a wind turbine gearbox (WTG) that consists of planetary gearsets. The model includes the nonlinear mesh stiffness of gears and the nonlinear stiffness of bearings as well as the flexibilities of the housing, planet carriers, and ring gears. The results presented state that if the optimal helix modification is applied, the edge loading of gear tooth ends will disappear and the contact pattern will improve significantly. This is why the face load factor will decrease and the gear safety factor will increase. However, the mesh load factor is decreased slightly after helix modification. This means that the helix modification is not directly related to the mesh load factor.

Wind Turbine Planetary Gearbox Fault Analysis Based on Bispectrum

Through researching the fault vibration signal characteristics of planetary gearbox, various forms of quadratic phase coupling phenomenon were found in the signals. Bispectrum has properties such as detecting the quadratic coupling phenomena and restraining gaussian noise signal, and it can be well used in the planetary gearbox fault diagnosis. Meanwhile, the sun gear distributed fault (gear wear) and partial fault (root crack, partial broken teeth) are simulated in the wind turbine test bench built in the laboratory. By analyzing the structure characteristics of its frequency through bispectrum, the fault of the gear is effectively diagnosed.

Effects of floating sun gear in a wind turbine's planetary gearbox with geometrical imperfections

This paper addresses the effect of gear geometrical errors in wind turbine planetary gearboxes with a floating sun gear. Numerical simulations and experiments are employed throughout the study. A National Renewable Energy Laboratory 750 kW gearbox is modelled in a multibody environment and verified using the experimental data obtained from a dynamometer test. The gear geometrical errors, which are both assembly dependent and assembly independent, are described, and planet-pin misalignment and eccentricity are selected as the two most influential and key errors for case studies. Various load cases involving errors in the floating and non-floating sun gear designs are simulated, and the planet-bearing reactions, gear vibrations, gear mesh loads and bearing fatigue lives are compared. All tests and simulations are performed at the rated wind speed. For errorless gears, the non-floating sun gear design performs better in terms of gear load variation, whereas the upwind planet bearing has more damage. In the floating sun gear scenario, the planet misalignment is neutralized by changing the sun motion pattern and the planet gear's elastic deformation. The effects of gear profile modifications are also evaluated, revealing that profile modifications such as crowning improve the effects of misalignment. Copyright © 2014 John Wiley & Sons, Ltd.

Iterative generalized synchrosqueezing transform for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions

The synchrosqueezing transform can effectively improve the readability of time–frequency representation of mono-component and constant frequency signals. However, for multi-component and time-variant frequency signals, it still suffers from time–frequency blurs. In order to address this issue, the synchrosqueezing transform is improved using iterative generalized demodulation. Firstly, the complex nonstationary signal is decomposed into mono-components of constant frequency by iterative generalized demodulation. Then, the instantaneous frequency of each mono-component is accurately estimated via the synchrosqueezing transform, by exploiting its merit of enhanced time–frequency resolution. Finally, the time–frequency representation of the original signal is obtained by superposing the time–frequency representations of all the mono-components with restored instantaneous frequency. This proposed method generalizes the synchrosqueezing transform to multi-component and time-variant frequency signals, and it has fine time–frequency resolution and is free of cross-term interferences. The proposed method was validated using both numerically simulated and lab experimental vibration signals of planetary gearboxes under nonstationary conditions. The time-variant planetary gearbox characteristic frequencies were effectively identified, and the gear faults were correctly diagnosed.

Complex signal analysis for wind turbine planetary gearbox fault diagnosis via iterative atomic decomposition thresholding

The vibration signals from complex structures such as wind turbine (WT) planetary gearboxes are intricate. Reliable analysis of such signals is the key to success in fault detection and diagnosis for complex structures. The recently proposed iterative atomic decomposition thresholding (IADT) method has shown to be effective in extracting true constituent components of complicated signals and in suppressing background noise interferences. In this study, such properties of the IADT are exploited to analyze and extract the target signal components from complex signals with a focus on WT planetary gearboxes under constant running conditions. Fault diagnosis for WT planetary gearboxes has been a very important yet challenging issue due to their harsh working conditions and complex structures. Planetary gearbox fault diagnosis relies on detecting the presence of gear characteristic frequencies or monitoring their magnitude changes. However, a planetary gearbox vibration signal is a mixture of multiple complex components due to the unique structure, complex kinetics and background noise. As such, the IADT is applied to enhance the gear characteristic frequencies of interest, and thereby diagnose gear faults. Considering the spectral properties of planetary gearbox vibration signals, we propose to use Fourier dictionary in the IADT so as to match the harmonic waves in frequency domain and pinpoint the gear fault characteristic frequency. To reduce computing time and better target at more relevant signal components, we also suggest a criterion to estimate the number of sparse components to be used by the IADT. The performance of the proposed approach in planetary gearbox fault diagnosis has been evaluated through analyzing the numerically simulated, lab experimental and on-site collected signals. The results show that both localized and distributed gear faults, both the sun and planet gear faults, can be diagnosed successfully.

Fault diagnosis of wind turbine planetary gearbox under nonstationary conditions via adaptive optimal kernel time–frequency analysis

Planetary gearboxes play an important role in wind turbine (WT) drivetrains. WTs usually work under time-varying running conditions due to the volatile wind conditions. The planetary gearbox vibration signals in such an environment are hence highly nonstationary. Conventional spectral analysis and demodulation analysis methods are unable to identify the characteristic frequency of gear fault from such nonstationary signals. As such, this paper presents a time–frequency analysis methods to reveal the constituent frequency components of nonstationary signals and their time-varying features for WT planetary gearbox monitoring. More specifically, we exploit the adaptive optimal kernel (AOK) method for this challenging application because of its fine time–frequency resolution and cross-term free nature, as demonstrated by our simulation analysis. In this study, the AOK method has been applied to identify the time-varying characteristic frequencies of gear fault or to extract different levels of impulses induced by gear faults from lab WT experimental signals and in-situ WT signals under time-varying running conditions. We have demonstrated that the AOK is effective diagnosis of: (a) both the local damage (a single chipped tooth) and distributed faults (wear of all teeth), (b) both sun gear and planet gear faults, and (c) faults with very weak signature (e.g., the sun gear fault at the low speed stage of a WT planetary gearbox).

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

In wind turbine planetary gearbox, the ability to identify ring-planet-sun gears meshing stiffness from the real data of vibration responses makes it possible to determine the physical existed planet gear defects which are used for severity assessments. There are limitations for vibration based gear diagnostic methods. Vibrations are secondary effects in the sense that they are dynamic responses of a gearbox excited by meshing stiffness and other excitations. The aim of this work is to diagnose the level of defects (gear tooth crack, gear tooth spalling and gear tooth breakage) in wind turbine gearbox using gears meshing stiffness. Time-varying meshing stiffness is used, where, a technique consists of a non-linear numerical optimisation is applied. The optimisation uses a dynamic model of the gears mesh and forms an estimate of both time-varying and frequency-varying mesh stiffness that best corresponds to the given set of vibration data. Multi-hour tests were conducted and recordings were acquired using translation vibration monitoring, where the optimum meshing stiffness was computed. The optimum meshing stiffness with the recording time was highlighted suggesting critical changes in the operation of the gearbox. The results indicate that if one tooth is defected due to cracks, spalling and breakage to reach the setting maximum level, the maintenance regime will be called despite a new damage is prone in the adjacent tooth to save time and cost.