Diagnostics and prognostics of planetary gearbox using CWT, auto regression (AR) and K-means algorithm

Abstract Condition monitoring of machine is recognized as effective strategy for undertaking the maintenance in wide variety of industries. Planetary gearbox is a critical component in helicopters, wind turbines, hybrid vehicles and so forth. Planetary gearbox are complex in nature due to its size and meshing components. Condition monitoring and fault diagnosis of planetary gearbox is challenging due to complexity in dependable fault extraction from raw vibration signal. The mechanism of planetary gearbox is complex as there are several gears meshing at the same time. To find out the nature of fault and defective component in planetary gearbox is difficult. In this paper, the fault detection and fault type identification diagnostic approach using auto regression model (AR) and continuous wavelet transforms (CWT) by considering different frequency range is established. The experimental research conducted with different type of fault vibration signals in the gearbox have been diagnosed and identified the fault type using AR Modelling, Impulse and Shape Factor for validation purposes. The unique behaviors and fault characteristics of planetary gearboxes are identified and analyzed. The fault frequency identification and extraction of features from the non-stationary signals in different fault severity level of vibration data demonstrates the reliability of proposed method. The developed algorithm adds efficacy in detecting the nature of fault and defective component without performing a visual inspection.

[1]  G. Dalpiaz,et al.  Condition monitoring indicators for pitting detection in planetary gear units , 2020 .

[2]  H. Zheng,et al.  GEAR FAULT DIAGNOSIS BASED ON CONTINUOUS WAVELET TRANSFORM , 2002 .

[3]  Jia-Zhong Wang,et al.  Gearbox Fault Diagnosis Based on Wavelet-AR Model , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[4]  Andrew Kusiak,et al.  Prognosis of the Remaining Useful Life of Bearings in a Wind Turbine Gearbox , 2016 .

[5]  Nilson Barbieri,et al.  Analysis of automotive gearbox faults using vibration signal , 2019, Mechanical Systems and Signal Processing.

[6]  Qiao Sun,et al.  SINGULARITY ANALYSIS USING CONTINUOUS WAVELET TRANSFORM FOR BEARING FAULT DIAGNOSIS , 2002 .

[7]  Peter W. Tse,et al.  Use of autocorrelation of wavelet coefficients for fault diagnosis , 2009 .

[8]  Jing Na,et al.  Vibration separation technique based localized tooth fault detection of planetary gear sets: A tutorial , 2019, Mechanical Systems and Signal Processing.

[9]  Jien-Chen Chen,et al.  Continuous wavelet transform technique for fault signal diagnosis of internal combustion engines , 2006 .

[10]  Salah Bouhouche,et al.  Application of Wavelet Transform for Fault Diagnosis in Rotating Machinery , 2012 .

[11]  Alaa Mohamed Riad,et al.  Prognostics: a literature review , 2016, Complex & Intelligent Systems.

[12]  M. Farid Golnaraghi,et al.  Assessment of Gear Damage Monitoring Techniques Using Vibration Measurements , 2001 .

[13]  Faris Elasha,et al.  Planetary bearing defect detection in a commercial helicopter main gearbox with vibration and acoustic emission , 2018 .

[14]  Jérôme Antoni,et al.  Vibration based condition monitoring of a multistage epicyclic gearbox in lifting cranes , 2014 .

[15]  David,et al.  A hybrid prognostic methodology for tidal turbine gearboxes , 2017 .

[16]  T R Pattabiraman,et al.  Assessment of sideband energy ratio technique in detection of wind turbine gear defects , 2015 .

[17]  Fengshou Gu,et al.  Early Fault Diagnosis for Planetary Gearbox Based Wavelet Packet Energy and Modulation Signal Bispectrum Analysis , 2018, Sensors.

[18]  Martin Vetterli,et al.  Adaptive wavelet thresholding for image denoising and compression , 2000, IEEE Trans. Image Process..

[19]  Ming J. Zuo,et al.  Planetary Gearbox Fault diagnosis via Joint Amplitude and Frequency Demodulation Analysis Based on Variational Mode Decomposition , 2017 .

[20]  Liu Hong,et al.  Vibration Based Diagnosis for Planetary Gearboxes Using an Analytical Model , 2016 .

[21]  Qiang Miao,et al.  Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators , 2018, IEEE Access.

[22]  Li Li,et al.  Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method , 2011 .

[23]  K. I. Ramachandran,et al.  Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN) , 2010, Expert Syst. Appl..

[24]  Irem Y. Tumer,et al.  USING TRIAXIAL ACCELEROMETER DATA FOR VIBRATION MONITORING OF HELICOPTER GEARBOXES , 2001 .

[25]  Wenyi Wang,et al.  Autoregressive Model-Based Gear Fault Diagnosis , 2002 .

[26]  Nader Sawalhi,et al.  Vibration Sideband Modulations and Harmonics Separation of a Planetary Helicopter Gearbox with Two Different Configurations , 2016 .

[27]  Chang Liu,et al.  A New Ensemble Fault Diagnosis Method Based on K-means Algorithm , 2012 .

[28]  Emine Ayaz,et al.  Autoregressive modeling approach of vibration data for bearing fault diagnosis in electric motors , 2014 .

[29]  Giorgio Dalpiaz,et al.  Effectiveness and Sensitivity of Vibration Processing Techniques for Local Fault Detection in Gears , 2000 .

[30]  Viliam Makis,et al.  Autoregressive model-based gear shaft fault diagnosis using the Kolmogorov–Smirnov test , 2009 .

[31]  B. Samanta,et al.  Gear fault detection using artificial neural networks and support vector machines with genetic algorithms , 2004 .

[32]  Mir Mohammad Ettefagh,et al.  Asynchronous input gear damage diagnosis using time averaging and wavelet filtering , 2008 .

[33]  Qiang Miao,et al.  Planetary Gearbox Vibration Signal Characteristics Analysis and Fault Diagnosis , 2015 .

[34]  Li Cao,et al.  Vibration and noise characteristics of a gear reducer under different operation conditions , 2019, Journal of Low Frequency Noise, Vibration and Active Control.

[35]  Joseph Mathew,et al.  A COMPARISON OF AUTOREGRESSIVE MODELING TECHNIQUES FOR FAULT DIAGNOSIS OF ROLLING ELEMENT BEARINGS , 1996 .

[36]  Jizhuang Fan,et al.  Vibration Characteristics Analysis of Planetary Gears with a Multi-Clearance Coupling in Space Mechanism , 2018, Energies.

[37]  Jayant V. Kulkarni,et al.  Gear tooth fault detection by autoregressive modelling , 2013, 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT).

[38]  I. Trendafilova,et al.  Autoregressive modelling for rolling element bearing fault diagnosis , 2015 .

[39]  K. V. Gangadharan,et al.  Gear Fault Detection Using Vibration Analysis and Continuous Wavelet Transform , 2014 .

[40]  P. Tse,et al.  Machine fault diagnosis through an effective exact wavelet analysis , 2004 .

[41]  Guolin He,et al.  Vibration modulation sidebands mechanisms of equally-spaced planetary gear train with a floating sun gear , 2019, Mechanical Systems and Signal Processing.

[42]  Olivier Rioul,et al.  Fast algorithms for discrete and continuous wavelet transforms , 1992, IEEE Trans. Inf. Theory.

[43]  Fadi Al-Badour,et al.  Vibration analysis of rotating machinery using time-frequency analysis and wavelet techniques , 2011 .

[44]  Viliam Makis,et al.  Wavelet analysis with time-synchronous averaging of planetary gearbox vibration data for fault detection and diagnostics , 2011, 2011 IEEE International Conference on Computer Science and Automation Engineering.

[45]  Robert B. Randall,et al.  Unsupervised noise cancellation for vibration signals: part I—evaluation of adaptive algorithms , 2004 .

[46]  Ming J. Zuo,et al.  Amplitudes of characteristic frequencies for fault diagnosis of planetary gearbox , 2018, Journal of Sound and Vibration.

[47]  Tarun Gupta,et al.  Fault Diagnosis Using Clustering. What Statistical Test to use for Hypothesis Testing? , 2019, Machine Learning and Applications: An International Journal.

[48]  J. Rafiee,et al.  Application of mother wavelet functions for automatic gear and bearing fault diagnosis , 2010, Expert Syst. Appl..