Maximum probable life time analysis under the required time-dependent failure probability constraint and its meta-model estimation

This paper establishes a dynamic Bayesian network to model the growth of corrosion defects on energy pipelines. The integrated model characterizes the growth of defect depth by a homogeneous gamma process and considers the biases and random errors associated with the in-line inspection (ILI) tools. The distributions of the mean value and coefficient of variation of the annual growth of defect depth are learned from multiple ILI data using the parameter learning technique of Bayesian networks. With the same technique, the distributions of the biases and standard deviation of random errors associated with ILI tools are learned from ILI data and their corresponding field measurements. An example with real corrosion management data is used to illustrate the process of developing the model structure, learning model parameters and predicting the corrosion growth and time-dependent failure probability. The results indicate that the model can in general predict the growth of corrosion defects with reasonable accuracy and the ILI-reported and field-measured depth can be used to update the time-dependent failure probability in a near-real-time manner. In comparison with existing growth models, the graphical feature of Bayesian networks makes it more intuitive and transparent to users. The employment of parameter learning provides a semi-automated and convenient approach to elicit the probabilistic information from ILI and field measurement data. The above advantages will facilitate the application of the model in the practice of corrosion management in pipeline industry.

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Advanced time-dependent reliability analysis based on adaptive sampling region with Kriging model

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x20901981 ◽

2020 ◽

Vol 234 (4) ◽

pp. 588-600

Author(s):

Yan Shi ◽

Zhenzhou Lu ◽

Ruyang He

Keyword(s):

Reliability Analysis ◽

Failure Probability ◽

Surrogate Model ◽

Adaptive Sampling ◽

Computational Cost ◽

Kriging Model ◽

Time Dependent ◽

Computational Time ◽

Optimization Strategy ◽

Dependent Failure

Aiming at accurately and efficiently estimating the time-dependent failure probability, a novel time-dependent reliability analysis method based on active learning Kriging model is proposed. Although active surrogate model methods have been used to estimate the time-dependent failure probability, efficiently estimating the time-dependent failure probability by a fewer computational time remains an issue because screening all the candidate samples iteratively by the active surrogate model is time-consuming. This article is intended to address this issue by establishing an optimization strategy to search the new training samples for updating the surrogate model. The optimization strategy is performed in the adaptive sampling region which is first proposed. The adaptive sampling region is adjustable by the current surrogate model in order to provide a proper candidate samples region of the input variables. The proposed method employs the optimization strategy to select the optimal sample to be the new training sample point in each iteration, and it does not need to predict the values of all the candidate samples at every time instant in each iterative step. Several examples are introduced to illustrate the accuracy and efficiency of the proposed method for estimating the time-dependent failure probability by simultaneously considering the computational cost and precision.

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A time-dependent probabilistic fatigue analysis method considering stochastic loadings and strength degradation

Advances in Mechanical Engineering ◽

10.1177/1687814018785560 ◽

2018 ◽

Vol 10 (7) ◽

pp. 168781401878556 ◽

Cited By ~ 1

Author(s):

Chunbo Su ◽

Shui Yu ◽

Zhonglai Wang ◽

Zafar Tayyab

Keyword(s):

Density Estimation ◽

Failure Probability ◽

Safety Margin ◽

Estimation Method ◽

Kernel Density ◽

Strength Degradation ◽

Probability Density Functions ◽

Time Dependent ◽

Density Functions ◽

Dependent Failure

This article proposes two strategies for time-dependent probabilistic fatigue analysis considering stochastic loadings and strength degradation based on the failure transformation and multi-dimensional kernel density estimation method. The time-dependent safety margin function is first established to describe the limit state of the time-dependent failure probability for mechatronics equipment with stochastic loadings and strength degradation. Considering the effective safety margin points and the corresponding number of the load cycles, two strategies for transforming the time-dependent failure probability calculation to the static reliability calculation are then proposed. Multi-dimensional kernel density estimation method is finally employed to build the probability density functions and the reliability is estimated based on the probability density functions. An engineering case of a filtering gear reducer is presented to validate the effectiveness of the proposed methods both in computational efficiency and accuracy.

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