Low-Speed Bearing Fault Diagnosis Based on Permutation and Spectral Entropy Measures

Despite its influence on wind energy service life, condition-based maintenance is still challenging to perform. For offshore wind farms, which are placed in harsh and remote environments, damage detection is critically important to schedule maintenance tasks and reduce operation and maintenance costs. One critical component to be monitored on a wind turbine is the pitch bearing, which can operate at low speed and high loads. Classical methods and features for general purpose bearings cannot be applied effectively to wind turbine pitch bearings owing to their specific operating conditions (high loads and non-constant very low speed with changing direction). Thus, damage detection of wind turbine pitch bearings is currently a challenge. In this study, entropy indicators are proposed as an alternative to classical bearing analysis. For this purpose, spectral and permutation entropy are combined to analyze a raw vibration signal from a low-speed bearing in several scenarios. The results indicate that entropy values change according to different types of damage on bearings, and the sensitivity of the entropy types differs among them. The study offers some important insights into the use of entropy indicators for feature extraction and it lays the foundation for future bearing prognosis methods.

[1]  D. Abásolo,et al.  Entropy analysis of the EEG background activity in Alzheimer's disease patients , 2006, Physiological measurement.

[2]  Brian Boswell,et al.  A Markovian approach for modelling the effects of maintenance on downtime and failure risk of wind turbine components , 2016 .

[3]  Eugeniusz Rusiński,et al.  Condition Monitoring Considerations for Low-Speed Machines , 2015 .

[4]  A.K. Nandi,et al.  Detection and Classification of Rolling-Element Bearing Faults using Support Vector Machines , 2005, 2005 IEEE Workshop on Machine Learning for Signal Processing.

[5]  T. Inouye,et al.  Quantification of EEG irregularity by use of the entropy of the power spectrum. , 1991, Electroencephalography and clinical neurophysiology.

[6]  Xin Sun,et al.  Improved multi-scale entropy and it's application in rolling bearing fault feature extraction , 2020 .

[7]  Craig A.S. Moodie.  An investigation into the condition monitoring of large slow speed slew bearings , 2009 .

[8]  Wensheng Su,et al.  Condition monitoring and fault diagnosis methods for low-speed and heavy-load slewing bearings: a literature review , 2017 .

[9]  Wei Xue,et al.  A Noise Suppression Method of Ground Penetrating Radar Based on EEMD and Permutation Entropy , 2019, IEEE Geoscience and Remote Sensing Letters.

[10]  Buyung Kosasih,et al.  Integrated condition monitoring and prognosis method for incipient defect detection and remaining life prediction of low speed slew bearings , 2017 .

[11]  Yat-Sze Choy,et al.  Multi-feature entropy distance approach with vibration and acoustic emission signals for process feature recognition of rolling element bearing faults , 2018 .

[12]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[13]  Buyung Kosasih,et al.  Circular domain features based condition monitoring for low speed slewing bearing , 2014 .

[14]  I. Percival,et al.  A spectral entropy method for distinguishing regular and irregular motion of Hamiltonian systems , 1979 .

[15]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[16]  Wolfram Koepf,et al.  The ordinal Kolmogorov-Sinai entropy: A generalized approximation , 2017, Commun. Nonlinear Sci. Numer. Simul..

[17]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[18]  Tian Ran Lin,et al.  An adaptive sensitive frequency band selection method for empirical wavelet transform and its application in bearing fault diagnosis , 2019, Measurement.

[19]  Jie Chen,et al.  Incipient fault diagnosis of rolling bearings based on adaptive variational mode decomposition and Teager energy operator , 2020 .

[20]  Guichao Yang,et al.  Remaining Useful Life Assessment of Slewing Bearing Based on Spatial-Temporal Sequence , 2020, IEEE Access.

[21]  Urko Leturiondo Zubizarreta,et al.  Revisión de las técnicas de monitorización del estado de los rodamientos de las palas de los aerogeneradores , 2019 .

[22]  C. Januário,et al.  Renewable Energy Cost Analysis: Wind Power , 2012 .

[23]  M. Elforjani,et al.  Accelerated natural fault diagnosis in slow speed bearings with Acoustic Emission , 2010 .

[24]  George Judge,et al.  Permutation Entropy and Information Recovery in Nonlinear Dynamic Economic Time Series , 2019, Econometrics.

[25]  Hua Li,et al.  Enhanced Frequency Band Entropy Method for Fault Feature Extraction of Rolling Element Bearings , 2020, IEEE Transactions on Industrial Informatics.

[26]  Jie Chen,et al.  Degradation evaluation of slewing bearing using HMM and improved GRU , 2019, Measurement.

[27]  Svetlana Berdyugina,et al.  Stochastic entropy production in the quite Sun magnetic fields , 2018, Monthly Notices of the Royal Astronomical Society: Letters.

[28]  B. Pompe,et al.  Permutation entropy: a natural complexity measure for time series. , 2002, Physical review letters.

[29]  Li Zhang,et al.  Fault feature extraction of low speed roller bearing based on Teager energy operator and CEEMD , 2019, Measurement.

[30]  Rongjing Hong,et al.  Degradation trend estimation of slewing bearing based on LSSVM model , 2016 .

[31]  Yongyong He,et al.  Approximate Entropy Analysis of the Acoustic Emission From Defects in Rolling Element Bearings , 2012 .

[32]  Xiwen Qin,et al.  The Fault Diagnosis of Rolling Bearing Based on Ensemble Empirical Mode Decomposition and Random Forest , 2017 .

[33]  Wensheng Su,et al.  Application of Wavelet Packet Sample Entropy in the Forecast of Rolling Element Bearing Fault Trend , 2011, 2011 International Conference on Multimedia and Signal Processing.

[34]  Fouad Slaoui-Hasnaoui,et al.  Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges , 2014 .

[35]  Paul A. Lynn.  Onshore and Offshore Wind Energy: An Introduction , 2011 .

[36]  Chenghui Zhang,et al.  A multi-fault diagnosis method based on modified Sample Entropy for lithium-ion battery strings , 2020 .

[37]  ZHIQIANG HUO,et al.  Adaptive Multiscale Weighted Permutation Entropy for Rolling Bearing Fault Diagnosis , 2020, IEEE Access.

[38]  Wahyu Caesarendra,et al.  Condition monitoring of naturally damaged slow speed slewing bearing based on ensemble empirical mode decomposition , 2013 .

[39]  Kai Wang,et al.  Fault Diagnosis for Rolling Bearings Based on Composite Multiscale Fine-Sorted Dispersion Entropy and SVM With Hybrid Mutation SCA-HHO Algorithm Optimization , 2020, IEEE Access.

[40]  Marek Krynke,et al.  A catalogue capacity of slewing bearings , 2012 .

[41]  Hamed Azami,et al.  Dispersion Entropy: A Measure for Time-Series Analysis , 2016, IEEE Signal Processing Letters.

[42]  Arun K. Samantaray,et al.  Rolling element bearing fault diagnosis under slow speed operation using wavelet de-noising , 2017 .

[43]  Zepeng Liu,et al.  Vibration analysis for large-scale wind turbine blade bearing fault detection with an empirical wavelet thresholding method , 2020 .

[44]  Xinglin Li,et al.  Bearing Fault Diagnosis with Kernel Sparse Representation Classification Based on Adaptive Local Iterative Filtering-Enhanced Multiscale Entropy Features , 2019 .

[45]  Shan Wang,et al.  Early diagnosis of bearing faults using decomposition and reconstruction stochastic resonance system , 2020 .

[46]  Peng Ding,et al.  Life prediction of slewing bearing based on isometric mapping and fuzzy support vector regression , 2020 .

[47]  Hongchao Wang,et al.  Feature extraction of latent fault components of rolling bearing based on self-learned sparse atomics and frequency band entropy , 2020 .

[48]  Diego Sandoval,et al.  Trends in condition monitoring of pitch bearings , 2019 .

[49]  Wahyu Caesarendra,et al.  A Review of Feature Extraction Methods in Vibration-Based Condition Monitoring and Its Application for Degradation Trend Estimation of Low-Speed Slew Bearing , 2017 .

[50]  Buyung Kosasih,et al.  An application of nonlinear feature extraction - A case study for low speed slewing bearing condition monitoring and prognosis , 2013, 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[51]  Cheng Yang,et al.  Health condition identification for rolling bearing based on hierarchical multiscale symbolic dynamic entropy and least squares support tensor machine–based binary tree , 2020, Structural Health Monitoring.

[52]  Fengtao Wang,et al.  A review of current condition monitoring and fault diagnosis methods for low-speed and heavy-load slewing bearings , 2017, 2017 9th International Conference on Modelling, Identification and Control (ICMIC).

[53]  Michael I. Friswell,et al.  Defect Diagnosis for Rolling Element Bearings Using Acoustic , 2009 .

[54]  Zepeng Liu,et al.  A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings , 2020 .

[55]  Y N Pan,et al.  Spectral entropy: A complementary index for rolling element bearing performance degradation assessment , 2009 .

[56]  Robert X. Gao,et al.  Mechanical Systems and Signal Processing Approximate Entropy as a Diagnostic Tool for Machine Health Monitoring , 2006 .

[57]  M. Elforjani,et al.  Monitoring the Onset and Propagation of Natural Degradation Process in a Slow Speed Rolling Element Bearing With Acoustic Emission , 2008 .

[58]  Li Cheng,et al.  Fault Analysis for Low-Speed Heavy-Duty Crane Slewing Bearing Based on Wavelet Energy Spectrum Coefficient , 2017 .

[59]  Yu Li,et al.  Multispectral Image Segmentation Based on a Fuzzy Clustering Algorithm Combined with Tsallis Entropy and a Gaussian Mixture Model , 2019, Remote. Sens..

[60]  Robert B. Randall,et al.  Rolling element bearing diagnostics—A tutorial , 2011 .

[61]  Buyung Kosasih,et al.  Acoustic emission-based condition monitoring methods: Review and application for low speed slew bearing , 2016 .

[62]  Mahardhika Pratama,et al.  Parsimonious Network based on Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis , 2018, ArXiv.

[63]  P. Stephan Heyns,et al.  Low speed rolling bearing diagnostics using acoustic emission and higher order statistics techniques , 2018 .

[64]  C. P. Butterfield,et al.  Wind Turbine Design Guideline DG03: Yaw and Pitch Rolling Bearing Life , 2009 .