Life prediction of slewing bearing based on isometric mapping and fuzzy support vector regression

The monitoring data of slewing bearing is massive. In order to establish accurate life prediction model from complex vibration signal of slewing bearing, a life prediction method based on manifold learning and fuzzy support vector regression (SVR) is proposed. Firstly, the multiple features are extracted from time domain and time-frequency domain. Then isometric mapping (ISOMAP) is used to reduce high-dimensional features to low-dimensional features that can reflect degeneration of slewing bearing well. Finally, the fuzzy SVR is used to predict the life degradation trend of slewing bearing. The results show that: (1) Multi-feature fusion after ISOMAP can obtain more comprehensive degradation indicator. (2) The complexity of the life prediction model is simplified and the real-time life degradation trend of slewing bearing can be well predicted by fuzzy SVR, so it is very suitable to predict life degradation trend of slewing bearing based on massive data well. The time of prediction on average is reduced by 72.7%. The mean absolute error (MAE) and root mean square error (RMSE) of prediction are reduced by 73% and 59% respectively compared with traditional methods. The accuracy of prediction is greatly improved.

[1]  Wanzhong Zhao,et al.  Parametric optimization of novel electric–hydraulic hybrid steering system based on a shuffled particle swarm optimization algorithm , 2018, Journal of Cleaner Production.

[2]  Rongjing Hong,et al.  Multiple physical signals based residual life prediction model of slewing bearing , 2016 .

[3]  Brigitte Chebel-Morello,et al.  Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network , 2015 .

[4]  Wanzhong Zhao,et al.  Multidisciplinary optimization of electric-wheel vehicle integrated chassis system based on steady endurance performance , 2018, Journal of Cleaner Production.

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

[6]  Jong-Myon Kim,et al.  A Hybrid Prognostics Technique for Rolling Element Bearings Using Adaptive Predictive Models , 2018, IEEE Transactions on Industrial Electronics.

[7]  Noureddine Zerhouni,et al.  Remaining useful life estimation based on nonlinear feature reduction and support vector regression , 2013, Eng. Appl. Artif. Intell..

[8]  Galal Omer,et al.  Performance of Support Vector Machines and Artificial Neural Network for Mapping Endangered Tree Species Using WorldView-2 Data in Dukuduku Forest, South Africa , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[9]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[10]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[11]  Lotfi A. Zadeh,et al.  Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic , 1997, Fuzzy Sets Syst..

[12]  Rongjing Hong,et al.  Online residual useful life prediction of large-size slewing bearings—A data fusion method , 2017 .

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

[14]  Luca Quagliato,et al.  Run-out Based Crossed Roller Bearing Life Prediction by Utilization of Accelerated Testing Approach and FE Numerical Models , 2017 .

[15]  Chen Lu,et al.  Bearing remaining life prediction using Gaussian process regression with composite kernel functions , 2015 .

[16]  Li Xu,et al.  A SVR-Based Remaining Life Prediction for Rolling Element Bearings , 2015, Journal of Failure Analysis and Prevention.

[17]  D. T. Waite.  A periphyton growth substrate useful for nutrient impact studies in large rivers , 1979 .

[18]  M. Elforjani,et al.  Estimation of Remaining Useful Life of Slow Speed Bearings Using Acoustic Emission Signals , 2016 .

[19]  Haruo Nagatani.  Improved Method of Roller Bearing Fatigue Life Prediction under Edge Loading Conditions , 2010 .

[20]  Buyung Kosasih,et al.  Degradation Trend Estimation and Prognosis of Large Low Speed Slewing Bearing Lifetime , 2014 .

[21]  Jong-Myon Kim,et al.  A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models , 2018, Reliab. Eng. Syst. Saf..