A Fault Diagnosis Method for Satellite Flywheel Bearings Based on 3D Correlation Dimension Clustering Technology

Flywheel bearing is a key mechanical part of a satellite. Its health plays an important role in the fatigue life of the satellite. However, it is rather difficult to diagnose the health state of the bearings due to the complex satellite system. This paper attempts to propose a three-direction correlation dimension method to diagnose the bearings of a satellite flywheel at three typical states based on K-medoids clustering technology. A set of spatial spheres representing different bearings are modeled to recognize these three states of the bearings. To avoid misdiagnosis or loss of the bearing state, a twice-cluster scheme is employed. A series of tests is carried out to observe the effectiveness of the proposed method. The result shows that the proposed method is capable of diagnosing the different states of the bearings and its accuracy is higher than 99% at given conditions.

[1]  N. Tandon,et al.  A comparison of some vibration parameters for the condition monitoring of rolling element bearings , 1994 .

[2]  Jianxi Fu,et al.  Induction Motor Bearing Fault Detection Using a Fractal Approach , 2010 .

[3]  P. Grassberger,et al.  Characterization of Strange Attractors , 1983 .

[4]  Nadir Boutasseta,et al.  A new time-frequency method for identification and classification of ball bearing faults , 2017 .

[5]  Bing Li,et al.  Feature extraction for rolling element bearing fault diagnosis utilizing generalized S transform and two-dimensional non-negative matrix factorization , 2011 .

[6]  Qingbo He,et al.  Time–frequency manifold for nonlinear feature extraction in machinery fault diagnosis , 2013 .

[7]  Ali Soleimani,et al.  Early fault detection of rotating machinery through chaotic vibration feature extraction of experimental data sets , 2015 .

[8]  H. S. Kim,et al.  Nonlinear dynamics , delay times , and embedding windows , 1999 .

[9]  D. Ruelle,et al.  Fundamental limitations for estimating dimensions and Lyapunov exponents in dynamical systems , 1992 .

[10]  B. C. Nakra,et al.  Detection of defects in rolling element bearings by vibration monitoring , 1993 .

[11]  Nadège Bouchonneau,et al.  A review of wind turbine bearing condition monitoring: State of the art and challenges , 2016 .

[12]  Ming Hong,et al.  An investigation of rolling bearing early diagnosis based on high-frequency characteristics and self-adaptive wavelet de-noising , 2016, Neurocomputing.

[13]  Idriss El-Thalji,et al.  A summary of fault modelling and predictive health monitoring of rolling element bearings , 2015 .

[14]  Qingjin Peng,et al.  Crack detection in the rotor ball bearing system using switching control strategy and Short Time Fourier Transform , 2018, Journal of Sound and Vibration.

[15]  Preeti Arora,et al.  Analysis of K-Means and K-Medoids Algorithm For Big Data , 2016 .

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

[17]  Peter J. Rousseeuw,et al.  Clustering by means of medoids , 1987 .

[18]  Joseph Mathew,et al.  USING THE CORRELATION DIMENSION FOR VIBRATION FAULT DIAGNOSIS OF ROLLING ELEMENT BEARINGS—I. BASIC CONCEPTS , 1996 .

[19]  Hae-Sang Park,et al.  A simple and fast algorithm for K-medoids clustering , 2009, Expert Syst. Appl..

[20]  Li-Bin Liu,et al.  A-posteriori error estimation in maximum norm for a strongly coupled system of two singularly perturbed convection-diffusion problems , 2017, J. Comput. Appl. Math..

[21]  S. E. Khadem,et al.  Improving one class support vector machine novelty detection scheme using nonlinear features , 2018, Pattern Recognit..

[22]  Paolo Pennacchi,et al.  A new procedure for using envelope analysis for rolling element bearing diagnostics in variable operating conditions , 2013 .

[23]  Fanrang Kong,et al.  Adaptive Multiscale Noise Tuning Stochastic Resonance for Health Diagnosis of Rolling Element Bearings , 2015, IEEE Transactions on Instrumentation and Measurement.

[24]  Xia Wang,et al.  Fault diagnosis of diesel engine based on adaptive wavelet packets and EEMD-fractal dimension , 2013 .

[25]  Diego Cabrera,et al.  A review on data-driven fault severity assessment in rolling bearings , 2018 .

[26]  P. Grassberger,et al.  Measuring the Strangeness of Strange Attractors , 1983 .

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

[28]  Paolo Pennacchi,et al.  Diagnostics of gear faults based on EMD and automatic selection of intrinsic mode functions , 2011 .

[29]  Jaromir Kukal,et al.  Application of rotational spectrum for correlation dimension estimation , 2017 .

[30]  S. E. Khadem,et al.  Quantitative diagnosis for bearing faults by improving ensemble empirical mode decomposition. , 2018, ISA transactions.

[31]  Junyan Yang,et al.  Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension , 2007 .

[32]  Ming Liang,et al.  An adaptive SK technique and its application for fault detection of rolling element bearings , 2011 .

[33]  J. Rafiee,et al.  A novel technique for selecting mother wavelet function using an intelligent fault diagnosis system , 2009, Expert Syst. Appl..

[34]  Jin Chen,et al.  Noise resistant time frequency analysis and application in fault diagnosis of rolling element bearings , 2012 .

[35]  Dipen S. Shah,et al.  A Review of Dynamic Modeling and Fault Identifications Methods for Rolling Element Bearing , 2014 .

[36]  Joseph Mathew,et al.  USING THE CORRELATION DIMENSION FOR VIBRATION FAULT DIAGNOSIS OF ROLLING ELEMENT BEARINGS—II. SELECTION OF EXPERIMENTAL PARAMETERS , 1996 .

[37]  Sanjay H Upadhyay,et al.  Bearing performance degradation assessment based on a combination of empirical mode decomposition and k-medoids clustering , 2017 .

[38]  Shufeng Ai,et al.  EMD based envelope analysis for bearing faults detection , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[39]  W. Wang,et al.  The application of a correlation dimension in large rotating machinery fault diagnosis , 2000 .

[40]  Antoine Tahan,et al.  A comparative study between empirical wavelet transforms and empirical mode decomposition methods: application to bearing defect diagnosis , 2016 .