基于记忆双导向变异的故障检测器生成方法

人工免疫算法中的阴性选择算法已经成功应用于故障诊断领域。为了生成更加优良的故障检测器,提高算法性能,本文在传统阴性选择算法的基础上,提出了一种基于免疫记忆双导向变异的故障检测器生成方法。该算法定义了一个免疫记忆集合,把与自体集合或成熟检测器集合发生匹配的检测器放入到免疫记忆集合里,并对这一检测器进行双导向变异,以减少资源浪费和冗余的发生。本文数据采用实属编码形式。仿真结果表明,新算法在故障检测性能方面优于传统算法,具有较高的故障诊断率,算法平均运行时间明显缩短。本文为故障诊断方法的研究拓宽了道路,具有一定的理论和现实意义。

A Fault Detector Generation Method Based on the Dual-Oriented Variation of Memory

The Negative Selection Algorithm has been applied in the field of fault diagnosis successfully.To generate more excellent detectors and improve the algorithm's performance,in this paper,a new method based on the dual-oriented variation of the immune memory set for generating fault detectors is proposed.To reduce the redundancy information,this article defines an immune memory set and puts the self matching detectors or mature detectors into the immune memory set,and then performs dual-oriented variation to the detectors.The proposed method utilizes real number coding.The simulations demonstrate that the proposed method provides better results of fault diagnosis and lowers the omission rate.Moreover,the average running time is significantly shorter.From this perspective,the theme of this academic study has a particular theoretical value and is pragmatically significant.