Probabilistic fatigue life prediction of bearings via the generalized polynomial chaos expansion

다국어 초록 (Multilingual Abstract)

In this work, an uncertainty propagation method for the probabilistic fatigue life prediction is proposed on the basis of the generalized polynomial chaos theory, which takes into account the uncertainty of material parameters and can effectively estimate the probability density function and cumulative distribution function of the fatigue life of rolling bearings. First, posterior distributions of the material parameters are fitted by Bayesian theory and based on grey bootstrap sampling fatigue test data. Then, the collocation approximation method is used to solve the expansion coefficients. Furthermore, the polynomial chaos expansion under each stress level is substituted into the bearing fatigue life prediction model for the probabilistic fatigue life prediction. Finally, the proposed method is validited by rolling bearing fatigue tests under two loading conditions. Results demonstrated that the probabilistic fatigue lives of rolling bearings are well predicted by the proposed method under constant and variable loading conditions.

참고문헌 (Reference)

1 K. Sepahvand, "Uncertainty quantification in stochastic systems using polynomial chaos expansion" 2 (2): 305-353, 2010

2 X. Y. Long, "Uncertainty propagation method for probabilistic fatigue crack growth life prediction" 103 : 102268-, 2019

3 Laily Oktaviana ; Van-Canh Tong ; 홍성욱, "Skidding analysis of angular contact ball bearing subjected to radial load and angular misalignment" 대한기계학회 33 (33): 837-845, 2019

4 L. Quagliato, "Run-out based crossed roller bearing life prediction by utilization of accelerated testing approach and FE numerical models" 130 : 99-110, 2017

5 Mostafa Yakout ; A. Elkhatib ; M. G. A. Nassef, "Rolling element bearings absolute life prediction using modal analysis" 대한기계학회 32 (32): 91-99, 2018

6 G. Pang, "Research on fatigue life for rolling bearing based on multistage loading test" Dalian University of Technology 2016

7 Y. Li, "Research on accelerated lifetime test method for aeroengine mainshaft bearing" Harbin Institute of Technology 2018

8 J. Mi, "Reliability assessment of complex electromechanical systems under epistemic uncertainty" 152 : 1-15, 2016

9 Y. F. Li, "Reliability assessment for systems suffering common cause failure based on Bayesian networks and proportional hazards model" 36 (36): 2509-2520, 2020

10 Y. F. Li, "Reliability analysis of multi-state systems with common cause failures based on Bayesian network and fuzzy probability" 311 (311): 195-209, 2022

11 J. Mi, "Reliability analysis of complex multi-state system with common cause failure based on evidential networks" 174 : 71-81, 2018

12 J. Son, "Probabilistic surrogate models for uncertainty analysis : Dimension reduction-based polynomial chaos expansion" 121 (121): 1198-1217, 2020

13 B. Ge, "Probabilistic service life prediction updating with inspection information for RC structures subjected to coupled corrosion and fatigue" 238 : 112260-, 2021

14 S. P. Zhu, "Probabilistic physics of failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty" 146 : 1-12, 2016

15 Y. Ai, "Probabilistic modeling of fatigue life distribution and size effect of components with random defects" 126 : 165-173, 2019

16 Y. Z. Li, "Probabilistic modeling of fatigue crack growth and experimental verification" 118 : 104862-, 2020

17 J. Gao, "Probabilistic model of fatigue damage accumulation of materials based on the principle of failure probability equivalence" 28 : 659-667, 2020

18 X. Jin, "Probabilistic life prediction for FCG degradation process of turbine disc with small sample data" 120 (120): 105026-, 2020

19 D. Liao, "Probabilistic framework for fatigue life assessment of notched components under size effects" 181 : 105685-, 2020

20 D. Leonetti, "Probabilistic fatigue resistance model for steel welded details under variable amplitude loading-inference and uncertainty estimation" 135 : 105515-, 2020

21 S. Chahine, "Probabilistic analysis of fatigue crack growth using efficient surrogate model" 836 (836): 012002-, 2020

22 J. P. Dias, "Parametric probabilistic approach for cumulative fatigue damage using double linear damage rule considering limited data" 127 : 246-258, 2019

23 D. Xiu, "Numerical Methods for Stochastic Computations: A Spectral Method Approach" Princeton University Press 2010

24 Xu Xu ; Qiwen Xue ; Yiqian He, "Nonlinear fatigue life prediction model based on material memory" 대한기계학회 34 (34): 5029-5039, 2020

25 Wenxin Qiao ; Xianglong Ni ; Lei Wang ; Xin Lv ; Liwei Chen ; Fucheng Sun, "New degradation feature extraction method of planetary gearbox based on alpha stable distribution" 대한기계학회 35 (35): 1-19, 2021

26 Y. Gao, "Measurement of contact fatigue P-S-N curve for specially strengthened GCr15 steel balls" 8 : 30-31, 2005

27 Guang Zeng ; Chunjiang Zhao ; Xiaokai Yu ; Qiang Bian ; Zhigang Xiao ; Bingyang Liu, "Life extension analysis of high speed ball bearing based on multi-parameter coupling" 대한기계학회 35 (35): 1569-1581, 2021

28 H. Usabiaga, "Improving with probabilistic and scale features the basquin linear and bi-linear fatigue models" 116 (116): 104728-, 2020

29 C. Mallor, "Full second-order approach for expected value and variance prediction of probabilistic fatigue crack growth life" 133 : 105454-, 2020

30 Henry Ogbemudia Omoregbee ; P. Stephan Heyns, "Fault detection in roller bearing operating at low speed and varying loads using Bayesian robust new hidden Markov model" 대한기계학회 32 (32): 4025-4036, 2018

31 Jie Zhou ; Hong-Zhong Huang ; Zhaochun Peng, "Fatigue life prediction of turbine blades based on a modified equivalent strain model" 대한기계학회 31 (31): 4203-4213, 2017

32 J. F. Barbosa, "Fatigue life prediction of metallic materials considering mean stress effects by means of an artificial neural network" 135 : 105527-, 2020

33 고은수 ; 김문국 ; 김인걸 ; Min-Sung Kim, "Fatigue life prediction in frequency domain using thermal-acoustic loading test results of titanium specimen" 대한기계학회 34 (34): 4015-4024, 2020

34 R. S. Haridas, "Defect-based probabilistic fatigue life estimation model for an additively manufactured aluminum alloy" 798 : 140082-, 2020

35 S. Oladyshkin, "Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion" 106 : 179-190, 2012

36 Y. Du, "Comparison of stochastic fault detection and classification algorithms for nonlinear chemical processes" 106 : 57-70, 2017

37 J. Mi, "Belief universal generating function analysis of multi-state systems under epistemic uncertainty and common cause failures" 64 (64): 1300-1309, 2015

38 J. Guo, "Bayesian hierarchical model-based information fusion for degradation analysis considering non-competing relationship" 7 : 175222-175227, 2019

39 J. Mi, "An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertain-ties" 220 : 108295-, 2022

40 Mi Xiao ; Yongsheng Yi ; Jinhao Zhang ; Wei Li, "An effective method for quantifying and incorporating uncertainty in metamodel selection" 대한기계학회 33 (33): 1279-1291, 2019

41 W. Ahmad, "A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models" 184 : 67-76, 2018

42 K. R. Lyathakula, "A probabilistic fatigue life prediction for adhesively bonded joints via ANNs-based hybrid model" 151 : 106352-, 2021

43 A. Aeran, "A new nonlinear fatigue damage model based only on SN curve parameters" 103 : 327-341, 2017

44 Z. Peng, "A new approach to the investigation of load interaction effects and its application in residual fatigue life prediction" 25 (25): 672-690, 2016

45 Qibin Wang ; Bo Zhao ; Hongbo Ma ; Jiantao Chang ; Gang Mao, "A method for rapidly evaluating reliability and predicting remaining useful life using two-dimensional convolutional neural network with signal conversion" 대한기계학회 33 (33): 2561-2571, 2019

46 J. Guo, "A Bayesian approach for degradation analysis with individual differences" 7 : 175033-175040, 2019