Vibration diagnosis is one of the most common techniques in condition evaluation of wind turbine equipped with gearbox. On the other side, gearbox is one of the key components of wind turbine drivetrain. Due to the stochastic operation of wind turbines, the gearbox shaft rotating speed changes with high percentage, which limits the application of traditional vibration signal processing techniques, such as fast Fourier transform. This paper investigates a new approach for wind turbine high speed shaft gear fault diagnosis using discrete wavelet transform and time synchronous averaging. First, the vibration signals are decomposed into a series of subbands signals with the use of a multiresolution analytical property of the discrete wavelet transform. Then, 22 condition indicators are extracted from the TSA signal, residual signal, and difference signal. Through the case study analysis, a new approach reveals the most relevant condition indicators based on vibrations that can be used for high speed shaft gear spalling fault diagnosis and their tracking abilities for fault degradation progression. It is also shown that the proposed approach enhances the gearbox fault diagnosis ability in wind turbines. The approach presented in this paper was programmed in Matlab environment using data acquired on a 2 MW wind turbine.
Wind energy is currently the fastest growing renewable energy source in the world. By the end of 2014, the global total installed wind capacity had reached more than 369 GW with about 133 GW of wind energy installed in Europe alone [
In order to maintain the high availability of multistage gearbox wind turbine, condition monitoring of gearbox is essential. The major goal of wind turbine condition monitoring system (CMS) is to provide predictive, condition based maintenance that will improve safety, decrease maintenance costs, and increase system availability [
The remainder of the paper is organized as follows. In Section
Depending on wind speed, wind turbines operate in time varying conditions that make fault diagnosis complicated. In this context, the classical application of Fourier based spectrum methods for processing the time varying signals does not give reliable results. Application of wavelet transformation in signal processing for fault diagnosis is a well-known technique which overcomes the problems that other signal processing techniques have. Unlike spectral analysis that represents a signal as sum of sinusoidal functions, wavelet transform decomposes the signal into wavelets of various scales in time-domain with variable window sizes and revealing the local structure in time frequency domain. Wavelet transform, which is capable of providing both time and frequency domain information simultaneously has been successfully used in nonstationary vibration signal processing and fault diagnosis [
The wavelet transformation uses the basic wavelet functions, also referred to as mother wavelets, which can dilate-compress and translate domain based on two parameters: scale-frequency and translation-time shift in order to apply short windows at low scales-high frequencies and long windows at high scales-low frequencies. The latter provides greater resolution in time for high frequency components of a signal and greater resolution in frequency for low frequency components.
Fast Fourier transform is still the most used signal processing method for gearbox fault diagnosis that can be found in commercially available online monitoring systems. On the other side, time-frequency analyses methods have the ability to display both time information and frequency information of a signal. The multiresolution analysis ability of DWT makes it suitable for revealing fault related information from nonstationary signals acquired on rotating machineries [
Decomposition procedure of
More detailed analytical bases of the wavelet technique can be found in [
As mentioned, regular spectrum based methods are not reliable for wind turbine gearbox fault diagnosis. The majority of the energy in the healthy state of the gear is concentrated at the fundamental meshing frequency and its harmonic. If sampling of vibration signal is synchronized with the rotation of a selected shaft over many revolutions the result will be a signal called TSA signal [
The most important vibration spectral components for gear fault diagnosis are gear mesh frequencies, their harmonics and sidebands generated by modulation due to geometric errors of tooth profile, meshing errors, and gear distribute/localized fault. Sideband amplitudes around gear mesh frequencies remain constant over the time for gear in good condition. Therefore, the number and the amplitude of sideband could be used to indicate presence of fault in meshing gears [
Signals of interest and used CIs.
Signals type | Name of signal | Used CIs |
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TSA | TSA | RMS, nRMS, P2P, CF, SKW, KURT, DA1, EO, FM0, SLF, SI, ASB |
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Residual TSA | resTSA | NA4, NA4 |
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Difference TSA | diffTSA | ER, FM4, FM4 |
Processing flow chart for feature extraction methods for vibration analysis.
The following 22 CIs were extracted: root mean square (RMS) and normalized RMS (nRMS) [
NA4 [
DWT 3 level frequency bands.
Approximations “ |
Frequency band (kHz) | Details “ |
Frequency band (kHz) |
---|---|---|---|
|
(0–1.6) |
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(1.6–3.2) |
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(0–3.2) |
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(3.2–6.4) |
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(0–6.4) |
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(6.4–12.8) |
Approximation
In order to extract the appropriate condition indicators and evaluate their historical trends, we present in this section the wind turbine gearbox experiment and advantages of using TSA and residual and different signal. To evaluate the proposed approach, wind turbine gearbox RDLT vibration data history is used collected by a vibration data acquisition system. The vibration signals were acquired by accelerometers mounted on the gearbox housing close to HSS shaft supporting bearing. The rotational speed of the HSS shaft was obtained by tachometer. Moreover, the vibration and tachometer signals were sampled at 25.6 kHz with 10.24 seconds lengths where 25 sets of vibration and tachometers signals were analyzed over 4 months.
Furthermore, in order to analyze data under similar wind turbine load, data sets are classified. Only data sets from 0.7 MW to 1.4 MW are used while others were neglected. Gear meshing frequency (GMF) of HSS pinion and gear was 728 Hz with 28 Hz HSS shaft running speed. Wind turbine gearbox ratio is close to 1 : 110 rated at 2.0 MW nominal electric power. The gearbox is a three-stage unit, with the first stage being planetary and the remaining two stages being parallel gears. Wind turbine gearbox used for evaluation of selected CIs is three-stage design gearbox, composed of one planetary stage and two parallel shaft stages.
This design type is widely used configuration for wind turbine drivetrains, as shown in Figure
Wind turbine gearbox with one planetary and two parallel stages, adapted from [
HSS speed profile.
For specific span, there was approximately a 1.2 Hz HSS speed change. Even though the HSS shaft speed variation seems relatively small in this case, this kind of variation is large enough to create smearing in the frequency based spectrum analysis, especially at high orders of the HSS rotational frequency, such as the HSS gear meshing frequency and its harmonics.
Before the CIs are extracted, it is important to evaluate the performance of TSA and the resTSA to observe how the vibration signals change for undamaged and damaged HSS gear. TSA is first performed on the signal over 250 averages, taking the HSS shaft rotation as reference. The effectiveness of this technique in case with damaged HSS gear is shown in Figure
Power spectrum comparison between the regular signal and TSA signal for damaged HSS gear.
It shows that the asynchronous components as well as the noise floor are significantly reduced, while frequencies that are synchronous with the rotation of the HSS gear have emerged in the spectrum; that is, the GMF and its harmonics are clearly visible indicating gear meshing irregularities and fault presence.
Comparing regular and TSA power spectra at the high speed GMFs and high-order harmonics, the amplitude difference becomes significant, especially at the higher orders GMFs. Although the full frequency range spectrum is available, for clarity, only the first 5 kHz frequency range is shown in Figure
Residual signal of HSS gear (one complete revolution of HSS gear).
To evaluate the diagnosis capabilities in the case of HSS gear spalling fault, features were implemented and processed for wind turbine gearbox RDLT. Generally, features can be divided into two groups, statistical condition indicators and power condition indicators as follows: Statistical CIs offer an overview on the trend of signals and therefore on the operation condition of the machine. CIs RMS, nRMS, P2P, CF, SKW, KURT, DA1, EO, FM0, SLF, SI, Power in bands CIs or frequency domain based CIs are signature typical use as second approach prior to applying more advanced processing methods. These CIs allow tracking the changes in energy around some frequencies related to defects in gear components to be monitored. These CIs are extracted from TSA signal and have been designed for fault detection, based on the analysis presented earlier in this paper. FM0, SLF, SI, and
The historical trends of all the CIs defined in the earlier section are depicted in Figures
P2P over RDLT.
Sideband Level Factor over RDLT.
Sideband Index over RDLT.
M8A over RDLT.
The results illustrate that the statistical condition indicators RMS, nRMS, CF, SKW, KURT, DA1, EO, FM0,
From 18/06/2014 this indicator had linear increase, which may reflect the beginning of the deterioration of the HSS gear. The starting point of the fault on HSS gear is also confirmed by SLF and SI as power in band CIs which significantly increases from 23/06/2014, as shown in Figures
We also remark that the SLF and SI indicators are more pertinent when extracted from the approximation coefficient
We also remark that all other analyzed statistical and power in band CIs based on TSA signal do not reveal a linear increase over the wind turbine RDLT (fault progression).
The NA4 CIs extracted from residual signal do not reveal a linear increase over the wind turbine RDLT. In general, NA4 condition indicator was developed as one that is more sensitive to progressing damage. By normalizing the squared variance for gear in gsood condition
In general, CIs extracted form difference signal are designed to reveal change of GMF high-order sidebands. ER does not reveal a linear increase over the wind turbine RDLT and even increase after the replacement of gearbox. This is expected because ER is known as condition indicator tracking heavy wear, where more than one tooth on the gear is damaged. FM4 have shown similar behavior with higher rate of fluctuation.
One of the most important insights to be drawn from this work is choosing a suitable reference for TSA and CI that can lead to earlier diagnosis of wind turbine HSS gear spalling fault. This paper has shown that statistical CIs can provide global information about the condition of the gearbox at the same time being unreliable indicators of gear state condition. However, power in band CIs was able to provide specific information about the state of HSS gear. Results have shown the advantage of DWT filtering, using the TSA signal rather than using only the TSA signal, which improves the ability to diagnose earlier the fault signatures from the wind turbine gearbox. Also, results illustrate the following: Statistical condition indicators RMS, nRMS, CF, SKW, KURT, DA1, EO, FM0, Starting point of HSS gear fault is confirmed by SLF and SI as power in band CIs earlier than P2P, SLF, SI, and CIs that reveal useful information about HSS gear fault are more pertinent when extracted from the regular TSA and approximation coefficient CIs ER, FM4, M6A, DiffDA1, and DiffP2P based on the difference signal do not reveal a linear increase over the wind turbine RDLT.
New approach presented in this paper has demonstrated the usefulness in wind turbine HSS gear spalling fault diagnosis and in tracking its progression over the time. The diagnosis results were confirmed with a wind turbine field case by gearbox site inspection.
The authors declare that there is no conflict of interests regarding the publication of this paper.