Principal Component Analysis Based Gear Fault Diagnostics in Different Stages of a Multi-Stage Gearbox Subjected to Extensive Fluctuating Speeds

Multi-stage gearboxes are vulnerable to failures often due to the extreme operating conditions, which may result in long downtimes. The current investigation is intended to examine the fault diagnostic capabilities of the integrated vibro-acoustic condition monitoring scheme while diagnosing the local/lumped defects exist at different speed stages of a multi-stage gearbox subjected to fluctuating/varying speeds. Experiments are performed, and the raw vibration and acoustic signatures are acquired simultaneously from the three-stage spur gearbox. Later, the raw data signatures are processed individually through discrete wavelet transform, and various descriptive statistics are extracted. Further, feature-level fusion is executed to obtain the integrated vibro-acoustic feature vector set for various speed stages of the gearbox. Finally, the obtained integrated feature vector set is classified using principal component analysis (PCA). It is observed that PCA performed using the integrated vibro-acoustic scheme clearly distinguishes among the various damage severity levels of pinion tooth exist at different speed stages of the gearbox.

[1]  Emiliano Mucchi,et al.  On the identification of the angular position of gears for the diagnostics of planetary gearboxes , 2017 .

[2]  Samir Mustapha,et al.  Multi-Source Data Fusion for Classification of Surface Cracks in Steel Pipes , 2018 .

[3]  I. R. Praveen Krishna,et al.  Local fault detection in helical gears via vibration and acoustic signals using EMD based statistical parameter analysis , 2014 .

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

[5]  G. R. Sabareesh,et al.  Design and development of a wind turbine test rig for condition monitoring studies , 2015, 2015 International Conference on Industrial Instrumentation and Control (ICIC).

[6]  Vamsi Inturi,et al.  Bearing Fault Severity Analysis on A Multi-stage Gearbox Subjected to Fluctuating Speeds , 2020, Experimental Techniques.

[7]  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..

[8]  V. Sugumaran,et al.  Fault diagnosis of roller bearing using fuzzy classifier and histogram features with focus on automatic rule learning , 2011, Expert Syst. Appl..

[9]  Enrico Zio,et al.  Artificial intelligence for fault diagnosis of rotating machinery: A review , 2018, Mechanical Systems and Signal Processing.

[10]  Robert X. Gao,et al.  Multi-scale enveloping spectrogram for vibration analysis in bearing defect diagnosis , 2009 .

[11]  Miguel Angel Ferrer-Ballester,et al.  Review of Automatic Fault Diagnosis Systems Using Audio and Vibration Signals , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[12]  M. S. Safizadeh,et al.  Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell , 2014, Inf. Fusion.

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

[14]  Yibing Liu,et al.  Multi-fault detection and failure analysis of wind turbine gearbox using complex wavelet transform , 2016 .

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

[16]  A. Shelke,et al.  Damage Detection Using Dissimilarity in Phase Space Topology of Dynamic Response of Structure Subjected to Shock Wave Loading , 2018, Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems.

[17]  Vamsi Inturi,et al.  Evaluation of surface roughness in incremental forming using image processing based methods , 2020 .

[18]  C. James Li,et al.  Estimation of gear tooth transverse crack size from vibration by fusing selected gear condition indices , 2006 .

[19]  Emad Gad,et al.  Damage Severity Estimation of Timber Poles Using Stress Wave Propagation and Wavelet Entropy Evolution , 2021 .

[20]  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 .

[21]  Vamsi Inturi,et al.  Comprehensive fault diagnostics of wind turbine gearbox through adaptive condition monitoring scheme , 2021 .

[22]  Surajkumar G. Kumbhar,et al.  Fault Diagnostics of Roller Bearings using Dimension Theory , 2020 .

[23]  G. R. Sabareesh,et al.  Comparison of condition monitoring techniques in assessing fault severity for a wind turbine gearbox under non-stationary loading , 2019, Mechanical Systems and Signal Processing.

[24]  Vamsi Inturi,et al.  Supervised Feature Selection Methods for Fault Diagnostics at Different Speed Stages of a Wind Turbine Gearbox , 2020 .

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

[26]  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 .

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

[28]  K. Supradeepan,et al.  Characterisation and analysis of flow over two side by side cylinders for different gaps at low Reynolds number: A numerical approach , 2014 .

[29]  Vamsi Inturi,et al.  Integrated condition monitoring scheme for bearing fault diagnosis of a wind turbine gearbox , 2019, Journal of Vibration and Control.

[30]  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 .

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