Improved Fault Size Estimation Method for Rolling Element Bearings Based on Concatenation Dictionary

This paper offers a new perspective on the vibrations of discrete bearing faults by focusing on the micro-motion states of rolling elements in spall fault bearings and proposes an improved matching pursuit algorithm for quantitative diagnosis with a high accuracy of atom selection and calculation efficiency. The generation mechanism of the vibration response signal is explained by analyzing the micro-motion status when rolling elements passing through the spall. A concatenation dictionary composed of an impact dictionary as the higher level and step dictionary as the lower level is constructed based on the acceleration variation analysis of the rolling elements. The information output by the higher-level dictionary is used as the input information for the lower-level dictionary to extract the fault features. Only one iteration on the higher-level dictionary is necessary to extract the correct impact atoms, with all subsequent iterative steps assigned to the lower-level dictionary. The advantage is that the influence of high-energy impact components on the extraction of step atoms can be removed. Thereafter, the optimized algorithm based on the concatenation dictionary is applied to the analysis of simulation and experimental signals. The comparative analysis demonstrates that the effective quantitative diagnosis is obtained, while the diagnostic precision and calculation efficiency are improved.

[1]  Robert B. Randall,et al.  Signal Processing Tools for Tracking the Size of a Spall in a Rolling Element Bearing , 2011 .

[2]  Carl Q. Howard,et al.  A nonlinear dynamic vibration model of defective bearings – The importance of modelling the finite size of rolling elements , 2015 .

[3]  Hamid Moeenfard,et al.  Nonlinear dynamic modeling of surface defects in rolling element bearing systems , 2009 .

[4]  Peng Chen,et al.  Step-by-Step Fuzzy Diagnosis Method for Equipment Based on Symptom Extraction and Trivalent Logic Fuzzy Diagnosis Theory , 2018, IEEE Transactions on Fuzzy Systems.

[5]  Martin J. Dowling Application of non-stationary analysis to machinery monitoring , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[6]  Fulei Chu,et al.  HVSRMS localization formula and localization law: Localization diagnosis of a ball bearing outer ring fault , 2019, Mechanical Systems and Signal Processing.

[7]  Naim Baydar,et al.  A comparative study of acoustic and vibration signals in detection of gear failures using Wigner-Ville distribution. , 2001 .

[8]  Huaqing Wang,et al.  A Novel Feature Enhancement Method Based on Improved Constraint Model of Online Dictionary Learning , 2019, IEEE Access.

[9]  Peng Chen,et al.  Vibration-Based Intelligent Fault Diagnosis for Roller Bearings in Low-Speed Rotating Machinery , 2018, IEEE Transactions on Instrumentation and Measurement.

[10]  Fanrang Kong,et al.  Quantitative recognition of rolling element bearing fault through an intelligent model based on support vector regression , 2013, 2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP).

[11]  Hui Wang,et al.  An Adaptive Randomized Orthogonal Matching Pursuit Algorithm With Sliding Window for Rolling Bearing Fault Diagnosis , 2018, IEEE Access.

[12]  Ming Liang,et al.  Fault severity assessment for rolling element bearings using the Lempel–Ziv complexity and continuous wavelet transform , 2009 .

[13]  Carl Q. Howard,et al.  Analyses of contact forces and vibration response for a defective rolling element bearing using an explicit dynamics finite element model , 2014 .

[14]  Lin Liang,et al.  Quantitative diagnosis of a spall-like fault of a rolling element bearing by empirical mode decomposition and the approximate entropy method , 2013 .

[15]  Robert B. Randall,et al.  Simulating gear and bearing interactions in the presence of faults. Part I. The combined gear bearing dynamic model and the simulation of localised bearing faults , 2008 .

[16]  Jose Mathew,et al.  A theoretical model to predict the effect of localized defect on vibrations associated with ball bearing , 2010 .

[17]  Hong Jiang,et al.  A novel Switching Unscented Kalman Filter method for remaining useful life prediction of rolling bearing , 2019, Measurement.

[18]  Bo Zhang,et al.  Bearing Fault Diagnosis Under Variable Working Conditions Based on Domain Adaptation Using Feature Transfer Learning , 2018, IEEE Access.

[19]  Yu Guo,et al.  Double Impulses Extraction of Faulty Rolling Element Bearing Based on EEMD and Complex Morlet Wavelet , 2014 .

[20]  Yi Qin,et al.  A New Family of Model-Based Impulsive Wavelets and Their Sparse Representation for Rolling Bearing Fault Diagnosis , 2018, IEEE Transactions on Industrial Electronics.

[21]  Sadok Sassi,et al.  A Numerical Model to Predict Damaged Bearing Vibrations , 2007 .

[22]  I. Epps An investigation into vibrations excited by discrete faults in rolling element bearings , 1991 .

[23]  Robert B. Randall,et al.  Vibration response of spalled rolling element bearings: Observations, simulations and signal processing techniques to track the spall size , 2011 .

[24]  Guanghua Xu,et al.  A quantitative diagnosis method for rolling element bearing using signal complexity and morphology filtering , 2012 .