基于加权精英TLBO-SVM的化工过程故障诊断方法

发布时间：2018-05-29 20:19

本文选题：故障诊断 + 教学算法(TLBO)　；参考：《华东理工大学》2015年硕士论文

【摘要】：支持向量机作为机器学习方法中一种比较成熟的理论已经在实际化工生产中得到了越来越广泛的应用。本文主要提出了一种加权精英教学算法(WETLBO)并将其应用到在支持向量机的参数优化过程中,最后使用上述优化后的模式识别方法解决Tennessee Eastman实际化工故障诊断问题。主要工作如下：1.本文提出了一种改进的加权精英教学算法。为了进一步提高该算法在参数寻优中的运算效率和收敛速度,将精英思想引入到教学算法中,精英个体在整个算法中作为每次迭代过程中适应度值最高的若干个最优解将被直接保留至下一次的迭代运算中。并根据精英个体和普通个体不同的特性,在种群更新时对精英个体和普通个体分别采用不同的惯性权重变化策略。对测试函数寻优的结果验证了改进后算法的可行性及一定的全局搜索能力。2.支持向量机中的惩罚参数和核函数参数往往决定着分类器的性能。本文将支持向量机在训练过程中的参数选择问题转化为与适应度值相关的最优化问题,并使用加权精英教学算法用于最优解的计算,从而得到最优的支持向量机参数。3.针对美国Tennessee Eastman工厂的实际化工过程模型所产生的历史故障数据,本文使用支持向量机结合加权精英教学TLBO算法给出了具体的故障诊断和分析过程。结果表明,采用改进后的WETLBO算法优化惩罚参数和核函数参数后的支持向量分类机应用到故障数据分类中,相比于其他常见的参数优化算法方法而言具有较理想的分类效果。
[Abstract]:As a mature theory in machine learning, support vector machine (SVM) has been widely used in practical chemical production. In this paper, a weighted elite teaching algorithm (WETLBOA) is proposed and applied to the parameter optimization of support vector machine (SVM). Finally, the above optimized pattern recognition method is used to solve the problem of practical chemical fault diagnosis in Tennessee Eastman. The main work is as follows: 1. This paper presents an improved weighted elite teaching algorithm. In order to further improve the efficiency and convergence speed of the algorithm in parameter optimization, the elite idea is introduced into the teaching algorithm. Elite individuals in the whole algorithm as the highest fitness in each iteration process of several optimal solutions will be directly retained in the next iteration operation. According to the different characteristics of elite individuals and ordinary individuals, different inertial weight changing strategies are adopted for elite individuals and ordinary individuals respectively during population renewal. The result of optimizing test function verifies the feasibility of the improved algorithm and its global search ability. 2. The performance of classifier is often determined by penalty parameters and kernel function parameters in SVM. In this paper, the parameter selection problem of support vector machine in training process is transformed into an optimization problem related to fitness, and the weighted elite teaching algorithm is used to calculate the optimal solution, and the optimal support vector machine parameter .3 is obtained. In view of the historical fault data generated by the actual chemical process model of the Tennessee Eastman factory in the United States, this paper presents a specific fault diagnosis and analysis process using support vector machine (SVM) combined with the weighted elite teaching TLBO algorithm. The results show that the improved WETLBO algorithm can be applied to the classification of fault data by optimizing the penalty parameters and kernel function parameters, and it has a better classification effect than other common parameter optimization algorithms.
【学位授予单位】：华东理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TQ050.7;TP181

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