Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions

Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions.

[1]  Lifeng Xi,et al.  Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods , 2007 .

[2]  Zijian Qiao,et al.  SVD principle analysis and fault diagnosis for bearings based on the correlation coefficient , 2015 .

[3]  C. Spearman.  The proof and measurement of association between two things. , 2015, International journal of epidemiology.

[4]  Jiawei Han,et al.  Orthogonal Laplacianfaces for Face Recognition , 2006, IEEE Transactions on Image Processing.

[5]  Viliam Makis,et al.  A robust diagnostic model for gearboxes subject to vibration monitoring , 2006 .

[6]  Ruoyu Li,et al.  Rotational Machine Health Monitoring and Fault Detection Using EMD-Based Acoustic Emission Feature Quantification , 2012, IEEE Transactions on Instrumentation and Measurement.

[7]  Bin Zhang,et al.  Degradation Feature Selection for Remaining Useful Life Prediction of Rolling Element Bearings , 2016, Qual. Reliab. Eng. Int..

[8]  Douglas A. Reynolds,et al.  Speaker Verification Using Adapted Gaussian Mixture Models , 2000, Digit. Signal Process..

[9]  J. L. Dorrity,et al.  Automated Feature Selection for Embeddable Prognostic and Health Monitoring (PHM) Architectures , 2006, 2006 IEEE Autotestcon.

[10]  Yaguo Lei,et al.  An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings , 2015, IEEE Transactions on Industrial Electronics.

[11]  Shenfang Yuan,et al.  Damage evaluation by a guided wave-hidden Markov model based method , 2016 .

[12]  Noureddine Zerhouni,et al.  Enabling Health Monitoring Approach Based on Vibration Data for Accurate Prognostics , 2015, IEEE Transactions on Industrial Electronics.

[13]  Liang Guo,et al.  A recurrent neural network based health indicator for remaining useful life prediction of bearings , 2017, Neurocomputing.

[14]  Noureddine Zerhouni,et al.  Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression , 2015, IEEE Transactions on Instrumentation and Measurement.

[15]  Shenfang Yuan,et al.  On-line updating Gaussian mixture model for aircraft wing spar damage evaluation under time-varying boundary condition , 2014 .

[16]  Lei Hu,et al.  Modeling the Relationship between Vibration Features and Condition Parameters Using Relevance Vector Machines for Health Monitoring of Rolling Element Bearings under Varying Operation Conditions , 2015 .

[17]  P. J. Huber.  The behavior of maximum likelihood estimates under nonstandard conditions , 1967 .

[18]  L. Renaudin,et al.  Natural roller bearing fault detection by angular measurement of true instantaneous angular speed , 2010 .

[19]  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 .

[20]  Yu Wang,et al.  Health monitoring and fault detection using wavelet packet technique and multivariate process control method , 2014, 2014 Prognostics and System Health Management Conference (PHM-2014 Hunan).

[21]  Jianbo Yu,et al.  Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models , 2011 .

[22]  Ruqiang Yan,et al.  Bearing performance degradation evaluation using recurrence quantification analysis and auto-regression model , 2013, 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).

[23]  Olivier Darnis,et al.  Statistic-based spectral indicator for bearing fault detection in permanent-magnet synchronous machines using the stator current , 2014 .

[24]  Gang Niu,et al.  Intelligent condition monitoring and prognostics system based on data-fusion strategy , 2010, Expert Syst. Appl..

[25]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[26]  A.H. Nizar,et al.  Power Utility Nontechnical Loss Analysis With Extreme Learning Machine Method , 2008, IEEE Transactions on Power Systems.