Integrated condition monitoring scheme for bearing fault diagnosis of a wind turbine gearbox

Rolling element bearing faults of a laboratory scale wind turbine gearbox operating under nonstationary loads have been diagnosed using condition monitoring (CM) techniques such as vibration analysis, acoustic analysis, and lubrication oil analysis. Two local bearing faults, namely, bearing inner race fault and bearing outer fault are seeded in the gearbox. The raw data from these techniques are decomposed and wavelet approximation coefficients of level four (a4) are extracted using discrete wavelet transform (DWT). A plethora of statistical features is computed from the wavelet approximation coefficients and the most significant features are being identified by implementing the decision tree algorithm. The classification efficiencies of each of these CM techniques are compared by using the support-vector machine algorithm. Furthermore, an integrated CM scheme is developed by combining the individual CM techniques and the fault diagnosing ability of the integrated CM scheme is compared with the individual CM techniques. A principal component analysis-based approach is used as a feature classification algorithm and an input feature matrix is formed by combining the significant features extracted from vibration, acoustic, and lubrication oil analysis. It has been observed that the integrated CM scheme has provided better classification interpretations than the single CM techniques and it can be extended for real time fault diagnosis of a wind turbine gearbox.

[1]  Juha Miettinen,et al.  Detection of gear pitting failure progression with on-line particle monitoring , 2018 .

[2]  N. R. Sakthivel,et al.  Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine , 2011, Expert Syst. Appl..

[3]  Slim Soua,et al.  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 , 2013 .

[4]  P. K. Kankar,et al.  A multiscale permutation entropy based approach to select wavelet for fault diagnosis of ball bearings , 2015 .

[5]  Eric Bechhoefer,et al.  Lubrication Oil Condition Monitoring and Remaining Useful Life Prediction with Particle Filtering , 2020 .

[6]  Daniel Morinigo-Sotelo,et al.  Methodology for fault detection in induction motors via sound and vibration signals , 2017 .

[7]  Pavan Kumar Kankar,et al.  Novel ensemble techniques for classification of rolling element bearing faults , 2017 .

[8]  Satish C. Sharma,et al.  Fault diagnosis of ball bearings using machine learning methods , 2011, Expert Syst. Appl..

[9]  Sujatha Chandramohan,et al.  Detection and Diagnosis of Gear Tooth Wear through Metallurgical and Oil Analysis , 2010 .

[10]  Satish C. Sharma,et al.  Rolling element bearing fault diagnosis using wavelet transform , 2011, Neurocomputing.

[11]  Robert G. Vinson.  Rotating machine diagnosis using smart feature selection under non-stationary operating conditions , 2016 .

[12]  Radoslaw Zimroz,et al.  Diagnostics of Ball Bearings in Varying-Speed Motors by Means of Artificial Neural Networks , 2011 .

[13]  Ming Yang,et al.  A wavelet approach to fault diagnosis of a gearbox under varying load conditions , 2010 .

[14]  Zhongxiao Peng,et al.  An integrated approach to fault diagnosis of machinery using wear debris and vibration analysis , 2003 .

[15]  Aditya Sharma,et al.  Feature extraction and fault severity classification in ball bearings , 2016 .

[16]  S. N. Panigrahi,et al.  Automatic gear and bearing fault localization using vibration and acoustic signals , 2015 .

[17]  Wentao Hu,et al.  The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform , 2014 .

[18]  Theodoros Loutas,et al.  The combined use of vibration, acoustic emission and oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery , 2011 .

[19]  Jing Yuan,et al.  Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review , 2016 .

[20]  Rajesh Kumar,et al.  Outer race defect width measurement in taper roller bearing using discrete wavelet transform of vibration signal , 2013 .

[21]  Sanjay H Upadhyay,et al.  A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings , 2016 .

[22]  G. R. Sabareesh,et al.  Comparison of Conventional Method of Fault Determination with Data-Driven Approach for Ball Bearings in a Wind Turbine Gearbox , 2018 .

[23]  V. Sugumaran,et al.  Fault diagnostics of roller bearing using kernel based neighborhood score multi-class support vector machine , 2008, Expert Syst. Appl..

[24]  Zhongxiao Peng,et al.  A study of the effect of contaminant particles in lubricants using wear debris and vibration condition monitoring techniques , 2005 .

[25]  V. Sugumaran,et al.  Effect of number of features on classification of roller bearing faults using SVM and PSVM , 2011, Expert Syst. Appl..

[26]  G. R. Sabareesh,et al.  Fault Size Estimation Using Vibration Signatures in a Wind Turbine Test-rig☆ , 2016 .

[27]  Jyoti K. Sinha,et al.  Combined vibration and thermal analysis for the condition monitoring of rotating machinery , 2014 .

[28]  A. Mohanty,et al.  APPLICATION OF DISCRETE WAVELET TRANSFORM FOR DETECTION OF BALL BEARING RACE FAULTS , 2002 .

[29]  Anand Parey,et al.  Spur gear tooth root crack detection using time synchronous averaging under fluctuating speed , 2014 .

[30]  V. Sugumaran,et al.  Exploiting sound signals for fault diagnosis of bearings using decision tree , 2013 .

[31]  Organized Self-Sustained Oscillations of Turbulent Flows over an Open Cavity , 2008 .

[32]  V. Sugumaran,et al.  Precise wavelet for current signature in 3phi IM , 2010, Expert Syst. Appl..