基于反面选择原理的智能融合故障检测模型及其应用

针对反面选择算法用于故障检测所存在的局限性,提出了一种基于反面选择原理的智能融合故障检测模型.该模型利用人工免疫系统的反面选择原理来构建神经网络检测器,通过训练将系统的异常模式信息存储在分布的检测器中,根据检测器的激活来发现系统的故障.通过混沌时间序列的异常检测仿真实验,研究了模型参数对故障检测性能的影响.最后以发动机压气机失速检测实验为例,证实该方法对失速信号的模式特征具有较强的分辨能力,同时表明神经网络检测器比常规的二进制编码检测器具有更好的故障识别能力.

Intelligence fusion approach to fault detection based on negative selection principle and its application

In order to solve the limitations that exist in fault detection based on the negative selection algorithm,a fault detection model using the intelligence fusion approach based on negative selection principle is proposed.The principle and structure of the model are presented,and its training algorithm is derived.Taking the advantages of neural networks training,the information of anomalous patterns is stored in the distributed neural networks-based detectors.This model has the distinguished capability of adaptation,which is well suitable for dealing with practical problems under time-varying circumstances.A fault can be found out through the relevant activated detector.The simulations of anomaly detection in chaotic time series are carried out to investigate the effect of model parameters on the capability of fault detection.In the end the illustrative stall detection experiments of compressor in an aeroengine demonstrate that the proposed method can achieve precise discrimination in the pattern features of stall signals,which also testify that the neural networks-based detectors have better recognition ability than the binary encoding detectors.