Fault Size Estimation Using Vibration Signatures in a ... - CyberLeninka

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ScienceDirect Procedia Engineering 144 (2016) 305 – 311

12th International Conference on Vibration Problems, ICOVP 2015

Fault Size Estimation Using Vibration Signatures in a Wind Turbine Test-Rig Sailendu Biswal, Jithin Donny George, G.R Sabareesh* Department of Mechanical Engineering, BITS-Pilani Hyderabad Campus, Hyderabad,500078,India

Abstract Fault size evaluation has become more significant in recent years to determine the fault size or the severity of fault apart from the fault detection for prediction of remaining useful life. The present investigation focuses on the fault size estimation of gear root crack in a wind turbine test rig using vibration signatures. A wind turbine test rig was developed at BITS-Pilani, Hyderabad Campus to simulate the working of a wind turbine. Time domain vibration signals are acquired using accelerometers for the healthy as well as faulty components. Discrete Wavelet Transform of vibration signature is performed and features are extracted from the statistical analysis of wavelet coefficients and the extracted features are used as inputs in an ANN (Artificial Neural Network) to effectively predict the size of gear root crack. © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license © 2016 The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-reviewunder under responsibility of organizing the organizing committee of ICOVP Peer-review responsibility of the committee of ICOVP 2015 2015. Keywords: Wind Turbine, Vibration Signature, Discrete Wavelet Transform, Fault Size Estimation, ANN

1. Introduction Wind power has come up as an emerging source of renewable energy and it’s expected to amount to 25% of world’s total energy by 2035.However, the operating and maintenance cost of wind turbine is estimated to be 2035% of the total power generation cost during its lifetime. Condition Based Maintenance (CBM) has become more significant in wind turbine health monitoring due to its convincing ability to reduce Life Cycle Cost (LCC). Predictive maintenance of critical components of wind turbine such as gearbox , bearings, shaft has become matter

* Corresponding author. Tel.: +91-8185919590; fax: +91-40-66303655. E-mail address: [email protected]

1877-7058 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license

(http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of ICOVP 2015

doi:10.1016/j.proeng.2016.05.137

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of concern as unscheduled maintenance cost of these components will be significantly larger than the scheduled maintenance cost [1].The wind turbine gearbox is vulnerable towards failure due to high amount of load, low speed and both fluctuating speed and load[2].The critical components used in wind turbine gearbox is often susceptible to catastrophic failure due to development of localized faults .Gear faults such as wear ,misalignment etc. can be categorised as distributed fault whereas pitting ,chipping, crack etc. are categorized as local faults[3]. Local faults or cracks of varying sizes often occur in gears of wind turbine. Nature of fault as well as its intensity is characterized by the depth and width of these cracks. However it has become more significant to determine the fault size or the severity of fault apart from the fault detection for prediction of remaining useful life[4]. Signal processing of raw vibration data obtained from critical component has been a crucial factor in successful fault detection as well as in fault size estimation. Many research and investigations has been made for fault detection in gears as they are considered as one of the vital component in a wind turbine, automotive or other rotating machineries for power transmission. The fault detection becomes a challenging affair in a wind turbine gearbox due to presence of multi stage of gears and the fault features are covered up by background random noise and vibration [2]. The time domain analysis technique such as time synchronous averaging (TSA) technique was implemented [5-7] for gearbox fault diagnostics due to its ability to diminish the effects of vibration signal which are not synchronous to the shaft and gear mesh frequencies and at the same time can amplify required part of signal over the noise. Statistical parameters such as skewness, kurtosis, root mean square (rms) value, crest factor, shape factor, clearance factor, impulse factor etc. has been used for fault detection and fault severity prediction [3, 8].Apart from earlier mentioned statistical parameters some special statistical parameters which includes FM0,FM4,NA4,NB4,M6A etc. has become advantageous particularly for gear fault detection[3, 4, 7]. Frequency domain analysis such as cepstral analysis [9] and spectrum analysis [10] has been effectively implemented for gear fault detection. Frequency domain features such as energy ratio, energy operator, mean frequency, frequency centre, root mean square frequency, standard deviation frequency was used along with other time domain features for fault level estimation[3, 11]. Vibration signal obtained from a gear having local fault contains amplitude and phase modulation. Faulty teeth of a gear often gives rise to sidebands which results from modulation of gear meshing frequency. Sidebands occur around a central frequency and it spreads over a wide frequency range due to its short time period of impact. The central frequency is also known as carrier frequency. The sideband is the region of importance containing local fault features, thus a properly chosen narrow band spectrum analyser could be very useful for fault detection[12]. However recent fault detection techniques for wind turbine gear box fault detection gives a preference for time frequency analysis. Vibration signatures obtained from wind turbine gear box used to be nonstationary. The time frequency analysis is desirable for nonstationary signal analysis[13].The time frequency analysis techniques such as time-frequency demodulation [14],Wigner-Ville Distribution(WVD) [13], Empirical Mode Decomposition(EMD) [15],wavelet analysis[13, 16-20],Hilbert –Huang Transform[21, 22] etc. has been utilized for fault detection in gear. However, wavelet transformation is found to be a better time frequency analysis technique practised for nonstationary vibration signals from wind turbine. Fault diagnosis in wind turbine was performed based on Morlet wavelet transformation [13, 23] and multiwavelet denoising [24]. The fault diagnosis and fault size estimation of wind turbine components is a matter of great concern in order to provide an effective maintenance solution as well as it helps in reducing the overall operating and maintenance cost. The time frequency analysis of vibration signature is often accomplished by performing feature extraction [25], followed by feature classification. Artificial intelligent methods based on Artificial Neural Network (ANN)[26],Support Vector Machine(SVM) [8, 27, 28],Immune Genetic Algorithm(IGA)[29]etc. were successfully implemented for feature classification in wind turbine fault diagnosis. This paper discusses the fault size estimation for a gear root crack in a wind turbine test rig using Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN).

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2. Experimental Setup A wind turbine test rig was developed at BITS-Pilani Hyderabad Campus to perform condition monitoring studies as shown in Fig. 1. [30]. The wind turbine test rig comprises of three stage gear train having an overall speed ratio of 48:1. A 1hp, 3phase AC motor was used in place of generator to perform the experiment and the speed of the motor was controlled by variable frequency drive .Three test cases including one for healthy condition and other two cases for faulty condition were performed. In order to simulate the faulty condition, two different root cracks were introduced to the gear tooth individually. The difference in root cracks was characterized by the depth of the crack. Using wire cut EDM (Electro Discharge Machining) the cracks were created at the root of gear as shown in Fig. 2. (a) and (b). The crack parameters are specified in the Table 1. The experiment was conducted at 1200 rpm and the vibration signatures were collected using an industrial accelerometer and National Instrument Data Acquisition System (NI DAQ 9234) having 10000 sample length and with a sampling rate of 2400Hz .The pinion was having 20 teeth with module 3 , 200 pressure angle and involute profile. The Gear Meshing Frequency (GMF) was calculated to be 400Hz using GMF= (t*rpm)/60 where‘t’ is number of gear teeth and ‘rpm’ refers to the speed of pinion. For each test case, 80 number of discrete time domain signal samples were collected and these time domain vibration signals were further analyzed to predict the fault size in the gear of wind turbine test rig.

Fig. 1. Wind Turbine Test Rig

a

b

Fig. 2.(a) Pinion with 1.5mm Depth Root Crack ; (b) Pinion with 2.5mm Depth Root Crack

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Sailendu Biswal et al. / Procedia Engineering 144 (2016) 305 – 311 Table 1.Showing Fault Parameters Fault Type Root Crack-1 Root Crack-2

Length(l in mm) 30 30

Width(w in mm) 0.25 0.25

Depth(d in mm) 1.5 2.5

3. Wavelet Analysis 3.1. Continuous Wavelet Transform (CWT) The CWT of signal (t) is represented below (1) The mother wavelet

is described by (2)

Where τ is translation and s is scale. CWT is a highly redundant representation as it computes many more coefficients than necessary. In signal analysis, a compact representation is desirable to avoid the requirement of high computational capacity. DWT provides compact representation with minimum number of coefficients. In CWT, the translation and scaling were arbitrarily chosen where as in DWT the translation and scaling is mathematically restricted. DWT can be defined as CWT evaluated at specific scale and translation. In DWT the translation is proportional to width of wavelet. 3.2. Discontinuous Wavelet Transform (DWT) (3) The mother wavelet is described by (4) The features from raw vibration signals were extracted using multilevel decomposition and an orthogonal wavelet db5 (Daubeschies Wavelet) was used as mother wavelet.Db5 possess highest potential to extract the buried pattern from vibration signatures from gear box [31]. Daubeschies wavelet is an orthogonal wavelet having compact support as well as high vanishing moment. For an orthogonal wavelet, vanishing moment is coupled with compact support, so a high vanishing moment ensures smooth and wide wavelet. A multi-level wavelet decomposition consisting of five level was used to obtain the wavelet coefficients. The original signal was decomposed into approximation and details using wavelet analysis as shown in Fig. 3. , Fig. 4. and Fig. 5. respectively. Approximation and details were considered as output of a low pass and high pass filter respectively, whereas the approximation contains more useful information about the signal being analysed than the details [32] .The approximation coefficients of level 5 denoted as ‘a5’ were collected from wavelet analysis of vibration signal .Statistical analysis was performed on the approximation coefficients and statistical feature such as mean, standard deviation, variance , root mean square(rms) value, skewness , kurtosis, maximum value and minimum value were collected and these features will serve as input to ANN.

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Fig. 3. Signal Decomposition for Healthy Condition

Fig. 5. Signal Decomposition for Faulty Gear with 2.5mm Depth Crack

Fig. 4. Signal Decomposition for Faulty Gear with 1.5mm Depth Crack

Fig. 6. Process Flow of Crack Size Estimation

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4. Results and Discussion Machine learning algorithms such as Artificial Neural Network (ANN) are capable of predicting the faults based on their size. The fault size was predicted using a feed forward back propagating ANN with five hidden layers. Gradient descent with momentum algorithm was used to train the network. The use of gradient descent with momentum algorithm causes ease attainment of global minima [33]. The ANN had considered mean squared error between the neural network output and target values performance. The statistical features were used as input to the ANN. The ANN was trained using 168 sets of data. In each iteration gradient of mean squared error was evaluated using back propagation to adjust the weights and biases. The predicted values of fault depth were evaluated from the ANN. The percentage of inaccuracy between predicted and actual fault size was calculated as shown in the Table 2. below.

Table 2. Table Showing Predicted Fault Size and % of Error Sl. No

Actual Depth of Crack(mm)

Predicted Depth of Crack(mm)

% error

1 2

1.5 2.5

1.323 2.252

11.8% 9.92%

The features obtained from wavelet transform of time domain vibration signal were used as input to the ANN and the gear tooth root crack depth was predicted with 88.2% and 90.08% accuracy for 1.5mm depth and 2.5mm respectively. The results obtained will be helpful for determining the severity of fault as well as remaining useful life prediction.

4. Summary The fault size for different test cases of gear root crack in a wind turbine test rig were predicted using vibration analysis .The original signal was decomposed to get approximation and details using discrete wavelet analysis. Statistical analysis of approximation coefficients were performed to extract features. Using features as input to the ANN the fault size for gear root cracks were determined successfully. Acknowledgements This study was funded by RIG, BITS-Pilani, Hyderabad Campus, which is gratefully acknowledged. References [1] S. Soua, P. Van Lieshout, A. Perera, T.-H. Gan, and B. Bridge, Determination of the combined vibrational and acoustic emission signature of a wind turbine gearbox and generator shaft in service as a pre-requisite for effective condition monitoring, Renewable Energy,51 (2013) pp. 175-181. [2] L. Yang, L. Xie, J. Wang, D. Wang, and Q. Miao, Current Progress on Wind Turbine Gearbox Condition Monitoring and Health Evaluation,ASME 2013 International Mechanical Engineering Congress and Exposition,(2013) pp. V04BT04A047-V04BT04A047. [3] Y. Lei and M. J. Zuo, Gear crack level identification based on weighted K nearest neighbor classification algorithm, Mechanical Systems and Signal Processing, 23 (2009) 1535-1547.

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