Mathematical Problems in Engineering

Volume 2018, Article ID 5672171, 9 pages

https://doi.org/10.1155/2018/5672171

## An Efficient Reliability Analysis Method Combining Improved EIF Active Learning Mechanism and Kriging Metamodel

^{1}School of Automation, Department of Electrical Engineering, Northwestern Polytechnical University, Xi’an, Shaanxi 710129, China^{2}School of Electronics and Information, Northwestern Polytechnical University, Xi’an, Shaanxi 710129, China

Correspondence should be addressed to Weilin Li; moc.621@709niliewil

Received 26 May 2018; Revised 15 July 2018; Accepted 29 July 2018; Published 8 August 2018

Academic Editor: Giovanni Falsone

Copyright © 2018 Dawei Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Complex implicit performance functions widely exist in many engineering problems. The reliability analysis of these problems has always been a challenge. Using surrogate model instead of real performance function is one of the methods to solve this kind of problem. Kriging is one of the surrogate models with precise interpolation technique. In order to make the kriging model achieve higher accuracy using a small number of samples, i.e., improve its practicability and feasibility in practical engineering problems, some active learning equations are wildly studied. Expected improvement function (EIF) is one of them. However, the EIF has a great disadvantage in selecting the added sample point. Therefore, a joint active learning mechanism, J-EIF, is proposed to obtain the ideal added point. The J-EIF active learning mechanism combines the two active learning mechanisms and makes full use of the characters of kriging model. It overcomes the shortcoming of EIF active learning mechanism in the selection of added sample points. Then, using Monte Carlo Simulation (MCS) results as a reference, the reliability of two examples is estimated. The results are discussed showing that the learning efficiency and accuracy of the improved EIF are both higher than those of the traditional EIF.

#### 1. Introduction

Reliability analysis plays an increasingly important role in practical engineering problems [1]. However, most engineering problems are the complex implicit nonlinear problems. Therefore, the reliability analysis of these problems is a time-consuming process, and sometimes it is unacceptable or impossible. Simultaneously, the application of auxiliary tools in the large-scale projects is more extensive because it is difficult or impossible to obtain the explicit performance function of the systems involving implicit performance functions. However, in most cases, many complex engineering problems may need a long time to complete one time deterministic analysis with those auxiliary tools, not to mention the reliability analysis, of which the repeated calls of the performance functions are necessary. Therefore, how to use these auxiliary tools to efficiently calculate the reliability of these kinds of implicit practical engineering problems is always a challenge for engineers. So far, an effective way to solve the reliability of these problems is to establish the input-output relationship of these implicit problems, i.e., surrogate model, to simulate its behavior [2–5]. Then, combining the established surrogate model with the Monte Carlo Simulation (MCS) [6], the reliability of these complex problems can be efficiently calculated [7].

Several surrogate models, for instance, neural network [8–10], support vector machine [11–13], response surface [14–16], and kriging model [17–20], can be used to approximate the actual performance function for the implicit problems. Among them, kriging model has been received more attention in recent years due to its flexible application. Furthermore, the result of kriging’s prediction at a new sample point can be considered as a normal random variable, of which the mean value and variance are the kriging predictive value and the so-called kriging variance, respectively. Kaymaz [21] combined the kriging method with the response surface method to estimate the reliability of the system. Sakata et al. [22] used the kriging model approximation to deal with the problems involving system optimization and obtained good results.

For improving the approximating ability of the kriging model, some elaborate active learning functions are constructed and employed to update the kriging model in an iterative way. An expected improvement function (EIF) [23] is one of the active learning functions and widely used to improve the accuracy of the kriging model. Hu [24] employed the EIF-kriging model to estimate the reliability of the time-dependent problems. Yang [25] was inspired by the EIF active learning mechanism and then proposed a hybrid reliability analysis method considering both random and interval variables. After obtaining the high-precision kriging model, the reliability of the systems involving implicit problems can be easily estimated by the MCS method. Echard et al. [26] combined the kriging model and the MCS method and proposed an AK-MCS method; the high accuracy of the proposed method is demonstrated by several examples. Other studies on the kriging model can be found in [27–30].

For the EIF active learning mechanism, firstly it is needed to use the current best solution, usually the minimum value of the real system at current training sample points, to construct a learning function. Secondly, find the sample point that maximizes the EIF in a large number of samples; notice that this process is performed with the kriging model rather than real objective function. Finally, add this sample and its corresponding real function value to training samples to update the kriging model. Meanwhile, the above active learning will terminate when the iteration process satisfies a given convergence criterion. This paper studied the EIF active learning mechanism and found some shortcomings in the process of screening the added sample point. The improvement is made for EIF to optimize its learning process and then improve the prediction ability of the kriging model. In particular, a joint active learning mechanism, J-EIF, is proposed to obtain the ideal added point. The J-EIF active learning mechanism combines the two active learning mechanisms which have the same principle but different integral area. It overcomes the shortcoming of EIF active learning mechanism in the selection of added sample points.

It is well known that three key points should be considered in a surrogate model with high accuracy and efficiency. The first point is the accuracy of the metamodel itself, the second point is the performance of the active learning mechanisms, and the third is the selection of appropriate experimental design points. In this paper, the target of the research is to improve the performance of the active learning mechanisms to make the accuracy and efficiency of the surrogate model better, rather than the metamodel or experimental design points.

The remaining of this paper is organized as follows. A brief introduction of MCS procedure for reliability analysis and kriging metamodel is given in Section 2. In Section 3, a brief introduction and some discussion on the EIF are given and a J-EIF active learning mechanism is proposed in order to overcome the shortcoming of EIF active learning mechanism in the selection of added sample points. In Section 4, using the MCS procedure as the reference, the accuracy of the proposed method is fully illustrated with a numerical example in example 1. At the same time, its practicability and feasibility in practical engineering problems are demonstrated in example 2. The conclusions of this study are drawn in Section 5.

#### 2. Fundamental Theory

##### 2.1. MCS Procedure for Reliability Analysis

Suppose that is an -dimension input variables vector and is an approximate surrogate model of the implicit performance function. Then, the reliability can be expressed as follows:where is the given security threshold, which is used as a criterion for evaluating whether the system fails. If the response of the system exceeds the given security threshold, the system is invalid; otherwise, it is safe. Therefore, it plays an important role in reliability analysis. denotes the -dimension variable space, is the joint probability density function of , and is an indicator function and is given as follows:

It can be seen from (1) that the reliability is the expectation of the indicator function . Then, (1) can be rewritten as follows:where denotes the expectation operator and represents the th input sample.

##### 2.2. Kriging Metamodel

Use and to denote the input random vector and the output response, respectively. Then, the specific kriging model can be expressed as follows:where the first part is the parameter distribution of linear regression and it can be specifically expressed as ; represents the global trend of the model, and it is usually a constant value because it does not play a decisive role for the accuracy of model fitting [26]. In this paper, it is assumed that is equal to 1. denotes the trend coefficients vector. is a stationary Gaussian process with the following statistic characteristics:where denotes standard covariance operator. represents the spatial correlation function of any two sample points in sample space. There are many forms for correlation function; the Gaussian correlation function is employed in this paper and is expressed as follows:where is the number of design variables; and represent the th component of the vectors and , respectively; and is a correlation parameter that reflects the correlation between the samples.

Given group of experimental sample points and that the correspond function response vector is , then the unknown parameters and can be estimated by the following equations, respectively:where is an -dimensional unit column vector. is a correlation matrix containing the correlation between two experimental design samples; i.e., .

It can be seen from (6), (7), and (8) that both and are the functions of . Therefore, needs to be estimated firstly. It can be obtained by optimizing a maximum the following equation:where denotes the determinant value. Then, the liner unbiased prediction of an unknown sample vector and the corresponding prediction variance can be expressed as follows, respectively:where and is an -dimension column vector revealing the correlative relations between new sample vector and experimental sample vector . It can be expressed as follows:

It can be seen clearly that the prediction of the kriging model at a new sample vector can be considered as the mean value of a random variable following normal distribution with mean value and variance .

#### 3. Improved EIF Learning Mechanism

It can be seen from the above analysis that a good kriging prediction model is a guarantee of reliability estimations. The EIF active learning mechanism is a function which can improve the prediction ability of the kriging model by adding new sample to training samples. Next, a brief introduction is given on the EIF. Then, some discussion on the EIF is given and a J-EIF active learning mechanism is proposed.

##### 3.1. Review of EIF Active Learning Mechanism

The EIF is an active learning function used to select the location at which the value of EIF is the largest. Then, the corresponding sample point and its objective function value should be added to the training sample points to improve the prediction ability of the kriging model. Its learning mechanism is given as follows [31]:where is the expectation operator and is the minimum value chosen from the true objective function values at the training sample points. For simplicity, use to represent . The above expectation can be solved bywhere is a realization value of prediction and is the probability density function of the prediction . The final analytical solution of the above integral equation is given aswhere is the standard normal cumulative distribution function and is the standard normal probability density function. Then, the added point can be calculated by

This indicates that is a sample point at which the EIF is the maximum.

##### 3.2. Some Discussion and Improvement on the EIF

For simplicity, a single variable function is used in the following discussion. Assume that six experimental sample points , , , , , are used to construct a kriging predictor for as shown in Figure 1. It can be seen from Figure 1 that the standard error of the predictor at above six experimental samples does go to zero, which indicates that the variance at these sample points is close to zero and the prediction is close to a determined value, i.e., the corresponding objective function value. Standard error of the kriging predictor at sample points and is large because there are no sample points around them. It can be found that the larger the standard error of the predictor, the greater the ‘uncertainty’ of the predicted. Simultaneously, the corresponding PDFs at these sample points are more decentralized; i.e., the kriging variance is larger.