Reliability assessment using probabilistic support vector machines
Reliability assessment using probabilistic support vector machines
复制标题
使用概率支持向量机进行可靠性评估
DOI：
10.1504/ijrs.2013.056378
复制
发表时间：
2013
影响因子：
--
通讯作者：
S. Missoum
中科院分区：
文献类型：
--
作者：
Anirban Basudhar;S. Missoum
本文提出了一种使用概率支持向量机（PSVMs）计算故障概率的方法。支持向量机（SVM）由于其固有的优点，近年来在可靠性评估中得到了广泛的关注.具体来说，支持向量机允许明确地构建故障域的边界。此外，它们还为不连续性、二元响应和多种故障模式问题提供了技术解决方案。然而，基本的SVM边界可能是不准确的，因此导致错误的故障概率估计。本文提出了占的SVM边界的不准确性在计算基于蒙特卡罗的故障概率。这是使用PSVM实现的，PSVM提供了Monte Carlo样本的误分类概率。故障估计的概率是基于一个新的基于S形的PSVM模型沿着识别的一个区域的错误分类的概率是大的。通过构造，基于PSVM的失效概率总是比基于确定性SVM的概率估计更保守。
This paper presents a methodology to calculate probabilities of failure using Probabilistic Support Vector Machines (PSVMs). Support Vector Machines (SVMs) have recently gained attention for reliability assessment because of several inherent advantages. Specifically, SVMs allow one to construct explicitly the boundary of a failure domain. In addition, they provide a technical solution for problems with discontinuities, binary responses, and multiple failure modes. However, the basic SVM boundary might be inaccurate; therefore leading to erroneous probability of failure estimates. This paper proposes to account for the inaccuracies of the SVM boundary in the calculation of the Monte Carlo-based probability of failure. This is achieved using a PSVM which provides the probability of misclassification of Monte Carlo samples. The probability of failure estimate is based on a new sigmoid- based PSVM model along with the identification of a region where the probability of misclassification is large. The PSVM-based probabilities of failure are, by construction, always more conservative than the deterministic SVM-based probability estimates.