A Bayesian‐based Reliability Estimation Approach for Corrosion Fatigue Crack Growth Utilizing the Random Walk

Abstract

Structural health monitoring enables corrosion fatigue damage for in‐service structures to be evaluated and prognosis health management to perform. In this paper, a Bayesian inference method using random walks is implemented to estimate the reliability of structures such as the pipelines subjected to repeated pressurization cycles and corrosive agents. The proposed method eliminates the intermediate step in updating process and computes the cumulative distribution function instead of calculating probability density function of individual parameters in conventional ones, which is affordable for a routine program, especially convenient for practical engineering use in the field. As taking all relevant random variables into account, this approach could significantly reduce uncertainties associated. For illustration and validation purpose, both numerical and practical examples are demonstrated in details. Copyright © 2016 John Wiley & Sons, Ltd.

Reliability Analysis to a Satellite-Equipped Harmonic Gear Drive Subjected to Multisource Data and Imbalanced Information

“…The Metropolis random walk algorithm (MRWA) [30] is employed here to draw samples. Since the parameter vector Θ has been estimated, other reliability indexes could be evaluated by generating enough samples from the posteriors of parameters based on (13) and (14).…”

“…It should be noted that the information used to obtain the parameter posterior distribution from (12) and the reliability indexes from (13) and (14) is single-element based; namely, no additional information from interelements (e.g., other model outputs and system structural formation) is incorporated. Since the elements of a multilevel system are interconnected, it is desirable to aggregate all potential valuable information for reliability analysis and evaluation.…”

“…On this occasion, we first estimate the parameters based on the two different data bases (i.e., the native information and induced information) and then integrate them into a comprehensive one. The probability indexes derived from (13) and (14) are defined as basic probability indexes, and the attached probability indexes on the basis of the interelement information will be discussed in the next section.…”

“…Benefiting from the successful application of the Markov chain Monte Carlo (MCMC) methods in Bayesian inference, the choice of prior distribution is not limited within conjugate distributions and the modelling to multitype data has been significantly extended. Recent works include Li et al [8], Pan and Yontay [9], Guo et al [10] are well summarized in [11][12][13][14][15][16][17][18].…”

Reliability assessment of complex electromechanical systems: A network perspective

“…The assumptions the assumptions of above approaches may not conform to most real-life systems. 11 Dynamic models: Markov model 12,13 ; dynamic fault tree [14][15][16] ; Petri net 17,18 ; Bayesian network 6,19,20 ; multivalued decision diagram 21,22 ; universal generating functions [23][24][25] ; and stochastic process approach 26,27 The problem of combinatorial explosion is intractable. Some of them have been criticized for the high computational complexity.…”