Changcong Zhou

Northwestern Polytechnical University | NWPU · Department of Engineering Mechanics

probability in this paper provides a framework for the reliability assessment and design of composites.

To efficiently evaluate the influence of the distribution parameters of the input variables on the failure probability of engineering structures and improve the reliability and safety of engineering structures in a targeted manner, new methods for the global reliability sensitivity analysis (RSA) of distribution parameters are proposed in this stud...

In solving expensive multi-objective optimization problems, surrogate models have been widely investigated. The existing multi-objective algorithms adopting surrogate models can be classified into two categories: surrogate-assisted evolutionary algorithms (SAEAs) and surrogate-based optimization algorithms (SBAs). However, their efficiency and conv...

A new time‐dependent reliability analysis model with a mixture of random variables and interval variables is proposed in this paper. First, samples of random variables are generated according to their probability density functions. Then, the samples are substituted into the performance function to obtain the interval processes corresponding to thes...

This paper studies the effect of the dependence state between basic events (BEs) on fault tree analysis (FTA) when the probabilities of events are characterized by interval values. The well‐known Frèchent bounds are extended for modeling six different types of dependence states between BEs. Three indices, called average dependence effect (ADE), loc...

Uncertainty is widely present in composite structures, and the probabilistic model is one of the most common ways to describe uncertainty. In some engineering problems, incomplete knowledge leads to uncertainty in the distribution parameters of probabilistic models. In this paper, the uncertainty of the distribution parameters is described by inter...

This study proposes a novel sensitivity index to provide essential insights into numerical models whose inputs are characterized by intervals. Based on the interval model and its normalized form, the interval processes are introduced to define a new sensitivity index. The index can represent the individual or joint influence of the interval inputs...

In recent years, reliability analysis based on adaptive Kriging (AK) has been extensively studied. However, constructing the Kriging model of high-dimensional systems during adaptive learning faces huge computational challenges. This paper combines partial least squares (PLS) with AK to develop an efficient method for reliability analysis of high-d...

For efficiently and accurately estimating the failure probability and the further sensitivity index of the high-dimension structures, a novel AS-AK-MCS method is proposed in this work. This method fully employs the merits of the active subspace-based dimension reduction technique, the active learning (AL) Kriging surrogate model, and the Monte Carl...

Existing ensemble-learning methods for reliability analysis are usually developed by combining ensemble-learning with a learning function. A commonly used strategy is to construct the initial training set and the test set in advance. The training set is used to train the initial ensemble model, while the test set is adopted to allocate weight facto...

Purpose Global sensitivity can measure the influence of input variables on model responses and is of positive significance for the improvement design of structural systems. This work aims to study the global sensitivity of structural models by combining the active subspace theory and neural network. Design/methodology/approach This study aims to i...

The failure probability function (FPF) is a function of failure probability that varies with distribution parameters of random inputs, and is required in reliability-based optimization. To estimate the failure probability at each possible value of distribution parameters, it is necessary to conduct a great quantity of model evaluations. This result...

It is important to determine the safety lifetime of Multi-mode Time-Dependent Structural System (MTDSS). However, there is still a lack of corresponding analysis methods. Therefore, this paper establishes MTDSS safety lifetime model firstly, and then proposes a Kriging surrogate model based method to estimate safety lifetime. The first step of prop...

When dealing with complex reliability problems, the classical ensemble of surrogate models (CESM) has better ability of prediction and generalization than the individual surrogate model. However, since the CESM only considers the global errors of the sample set, the prediction results of the sample points far away from the region where most of the...

In the time-variant systems, random variables, stochastic processes, and time parameter are regarded as the inputs of time-variant computational model. This results in an even more computationally expensive model what makes the time-variant reliability analysis a challenging task. This paper addresses the problem by presenting an active learning st...

For the widely existing time-dependent structural system (TDSS), the current model ignores the interaction among multiple modes in the minimum transformation of the performance function of each mode w.r.t. the time, it may misjudge the state of the system at some instants and result in the error of estimating the reliability. To avoid this possible...

Uncertainties widely existing in composite structures make the reliability and sensitivity analysis highly necessary. In this work, the approach based on adaptive Kriging is discussed to estimate the failure probability, local and global sensitivity of composite structures. The local sensitivity can measure how the local perturbations of the distri...

A global sensitivity analysis (GSA) approach based on the theory of active subspaces and Kriging surrogate metamodeling is developed. Three GSA measures, namely the derivative-based global sensitivity measure (DGSM), activity score and Sobol’ total effect indices can be obtained at different steps of the proposed approach. Firstly, by estimating th...

It is necessary to execute the safety lifetime analysis to ensure the safety service of the structure. At present, the existing safety lifetime analysis methods used different learning functions to construct a Kriging model in the time interval of interest. The constructed Kriging model can estimate the time-dependent failure probability (TDFP) in...

In this paper, we propose derivative-oriented parametric sensitivity indices to investigate the influence of parameter uncertainty on a previously proposed failure probability-based importance measure in the presence of multidimensional dependencies. Herein, the vine copula function, a powerful mathematical tool for modeling variable dependencies,...

This paper proposes a new non-probabilistic time-dependent reliability model for evaluating the kinematic reliability of mechanisms when the input uncertainties are characterized by intervals. Based on the introduction of the non-probabilistic interval process of motion error, the most probable point of an outcrossing is defined to transform the co...

As an important part of the aircraft door, the relief valve mechanism plays an important role in maintaining the normal and safe operation of the aircraft. In this paper, the multi-body dynamical model of the relief valve mechanism is built in ADAMS to study the effect of pin wear on positioning accuracy. The performance function of the positioning...

A fault tree based system reliability method was developed to predict the failure probabilities of system components by a non-probability interval model. A non-probabilistic reliability index was developed which included reliability or safety criteria to evaluate the system reliability. Then, two sensitivity indices were developed to indicate the c...

In this work, the issue of uncertainty analysis of motion errors for mechanisms is studied. First, two definitions of motion errors, that is, maximum error and accumulative error, are introduced to measure the motion accuracy in a comprehensive way. Then, the method for performing an uncertainty analysis of the two errors is discussed. For the maxi...

For a certain type of aircraft landing gear retraction-extension mechanism, a multi-body dynamic simulation model is established, and the time-dependent curves of force and angle are obtained. Considering the random uncertainty of friction coefficient, assembly error, and the change of hinge wear under different retraction times, the reliability mo...

The time-dependent mechanism reliability and sensitivity analysis with imprecise probability distributions is investigated in this article. First, the interval model is employed to describe the uncertainty of the parameters of input variables due to the lack of data and knowledge. Next, the form of time-dependent failure probability and global reli...

The safety lifetime analysis under the constraint of a required time-dependent failure probability is of great significance to ensure the structural safety, and the vital point in safety lifetime analysis is efficiently estimating the time-dependent failure probability in any subinterval of the time interval of interest. For addressing this issue,...

This paper focuses on the issue of reliability and global sensitivity analysis for an airplane slat mechanism considering the uncertainties in the wear process of mechanical components. First, the multi-body kinematic model of the slat mechanism is built in the ADAMS software. The geometrical sizes of the roller wheels after wear degradation are co...

This article proposes an uncertainty analysis method for evaluating the reliability of a system and calculating the relative importance of the system inputs when the probabilities of basic events are characterized by intervals. The reliability requirement or the safety criterion is first considered in the system reliability assessment. A nonprobabi...

For uncertainty analysis of high-dimensional complex engineering problems, this article proposes a hybrid multiplicative dimension reduction method based on the existent multiplicative dimension reduction method. It uses the multiplicative dimension reduction method to approximate the original high-dimensional performance function which is sufficie...

Variance-based sensitivity analysis (SA) has been frequently applied to dynamic models with multivariate output. The generalized sensitivity indices are defined by combining principal components analysis with analysis of variance to synthesize the influence of each input on the whole dynamic output. In order to efficiently perform global SA on dyna...

The application of reliability analysis and reliability sensitivity analysis methods to complicated structures faces two main challenges: small failure probability (typical less than 10-5) and time-demanding mechanical models. This paper proposes an improved active learning surrogate model method, which combines the advantages of the classical Acti...

The complex layout and harsh working environment often encountered in aeronautical hydraulic pipeline system (AHPS) result in the requirement of a large number of constraints that are mainly utilized to avoid the damage caused by engine vibration and wing flutter. As a result, improvement of the mean time between failures (MTBF) of the AHPS conside...

This article investigates the design of constraint hoops in the aeronautical hydraulic pipeline system. Non-probabilistic sensitivity analysis is used to screen out the hoops which are insensitive to the maximum stress response, the maximum displacement response as well as the first-order natural frequency. The analysis result can give guidance to...

In an uncertainty scheme, reliability and global sensitivity analysis is studied in this work, to provide helpful information for probabilistic anti-resonance design of vibration systems. Discussions show that the resonance failure problem can be viewed as a series system, in which input uncertainties are modeled by random variables. In order to qu...

The sensitivity index plays a critical role in the design of product and is used to quantify the impact degree of the uncertainty of the input variable to the uncertainty of the interest output. This paper presents a new local reliability sensitivity method and a global reliability sensitivity analysis method of time-dependent reliability problems....

Systems with random variables and random excitations exist widely in various engineering problems. Extending the traditional global reliability sensitivity to this double-stochastic system has important guiding significance for its design optimization. However, because there is a certain coupling between the randomness of variables and the randomne...

This paper investigates the reliability design optimization of an aeronautical hydraulic pipeline system, in which the constraint locations are treated as design parameters. To reduce the size of the optimization problem, two non-probabilistic global sensitivity indices are introduced and modified to screen out those constraint locations which have...

Hybrid reliability analysis of motion mechanism, in which the inputs contain both random variables and interval variables, is investigated in this paper. Firstly, the hybrid uncertainty is divided into three categories. In the first category, the inputs that include both random variables and interval variables are considered. In the second category...

In this paper, a global sensitivity analysis based on variance is performed to study how the input uncertainty contributes to the output. In order to obtain the sensitivity indices efficiently and accurately, the sparse grid integration is introduced in accordance with the fact that variance-based sensitivity indices can be viewed as nested express...

Identifying the parameters that substantially affect the time-dependent reliability is critical for reliability-based design of motion mechanism. The time-dependent local reliability sensitivity and global reliability sensitivity are the two effective techniques for this type of analysis. This work extends the first-passage method and PHI2 method,...

In this work, we consider the interval uncertainty from the probabilistic point of view, focusing on the establishment of probabilistic representation of interval uncertainty. A model-free sampling technique is first introduced, which can be used to produce a considerably larger sample from a given small sample. To make sure the local statistical c...

Traditional Global Sensitivity Analysis (GSA) focuses on ranking inputs according to their contributions to the output uncertainty. However, information about how the specific regions inside an input affect the output is beyond the traditional GSA techniques. To fully address this issue, in this work, two regional moment-independent importance meas...

In this work, a new reliability method is proposed by combining the relevance vector machine (RVM) and importance sampling in a proper way. A modified Metropolis algorithm is utilized to generate the training data that covers the important area. With the training data, a surrogate model is built with RVM to approximate the limit state surface. Then...

Different importance measures exist in the literature, aiming to quantify the contributions of model inputs to the output uncertainty. Among them, the moment-independent importance measures consider the entire model output without dependence on any of its particular moments (e.g., variance), and have attracted growing attention among both academics...

To measure the effects of input variables’ realization on variance of the output performance function and on the failure probability of structural system, two new probabilistic importance measures (PIMs) are defined. As an input variable takes its realization according to its probability distribution, the two PIMs can quantify the possibility of re...

To quantitatively estimate the contributions of component failure modes to system output, failure-mode importance measures have been proposed in this paper. This issue is discussed in two schemes: in Scheme 1, failure-mode importance is measured by the change in the characteristics of the system output after removal of the interested failure mode;...

The moment independent importance measure is a popular global sensitivity analysis technique, and aims at evaluating contributions of the inputs to the whole output distribution. In this work, moment independent sensitivity analysis is performed for models with correlated inputs, by decomposing the importance measure into the uncorrelated part and...

To get a better understanding on the output uncertainty contributed by an individual variable as well as the correlated variables of models with dependent inputs, a method for decomposing Sobol's first-order effect indices into uncorrelated variations and correlated variations is investigated. Instead of using Monte Carlo simulation or full tensor...

Variance based sensitivity indices represent how the input uncertainty influences the output uncertainty. In order to identify how the distribution parameters of inputs influence the variance contributions, this work proposes the sensitivity of the variance contributions, which is defined by the partial derivative of the first-order variance contri...

For structural systems with both epistemic and aleatory uncertainties, research on quantifying the contribution of the epistemic and aleatory uncertainties to the failure probability of the systems is conducted. Based on the method of separating epistemic and aleatory uncertainties in a variable, the core idea of the research is firstly to establis...

The applicability of a relevance vector machine to structural reliability analysis is introduced in this work. Samples covering the important domains in the input space are first generated and selected by a modified Metropolis algorithm. Then, the samples are employed to build a surrogate model with the help of a relevance vector machine to approxi...

Through several decades of development, global sensitivity analysis has been developed as a very useful guide tool for assessing scientific models and has gained pronounced attention in environmental science. However, standard global sensitivity analysis aims at measuring the contribution of input variables to model output uncertainty on average by...

A novel adaptive importance sampling method is proposed to estimate the structural failure probability. It properly utilizes Markov chain algorithm to form an adaptive importance sampling procedure. The main concept is suggesting the proposal distributions of Markov chain as the importance sampling density. Markov chain states can adaptively popula...

To analyze the effect of the region of the model inputs on the model output, a novel concept about contribution to the sample failure probability plot(CSFP) is proposed based on the contribution to the sample mean plot(CSM) and the contribution to the sample variance plot(CSV). The CSFP can be used to analyze the effect of the region of the model i...

Importance analysis is aimed at finding the contributions of the inputs to the output uncertainty. For structural models involving correlated input variables, the variance contribution by an individual input variable is decomposed into correlated contribution and uncorrelated contribution in this study. Based on point estimate, this work proposes a...

Variance-based importance measure has proven itself as an effective tool to reflect the effects of input variables on the output. Owing to the desirable properties, researchers have paid lots of attention to improving efficiency in computing a variance-based importance measure. Based on the theory of point estimate, this article proposes a new algo...

To measure effects of the distribution parameters of input variables on the output response of engineering structures, the analysis methods are investigated to solve the sensitivity of the cumulative distribution function of the output response with respect to the input parameters. For a linear input–output response model with independently normal...

For structural system with multiple failure modes extensively present in engineering practice, effects of failure modes on the system uncertainties need to be considered, upon which the design work can be improved. Enlightened by the concept of importance measures for basic variables, three mode importance measures are established to reflect the ef...

For clearly exploring the origin of the variance of the output response in case the correlated input variables are involved, a novel method on the state dependent parameters (SDP) approach is proposed to decompose the contribution by correlated input variables to the variance of output response into two parts: the uncorrelated contribution due to t...

For searching the stiffness structure under the constraints of given material volume fraction, the subset simulationbased topology optimization method is established on the combination of the advanced sensitivity filtering technique with the subset simulation, an efficient reliability analysis method. The subset simulation-based topology optimizati...