基于数值微分的树突状细胞故障检测方法

Dendritic Cell Fault Detection Method Based on Numerical Differentiation

针对现有树突状细胞算法(dendritic cell algorithm,DCA)在不同类型设备的故障检测中严重依赖人工经验定义输入信号,缺乏适应性和完备性,提出了一种基于数值微分的树突状细胞故障检测模型——NDDC-FD.首先,引入变化是系统危险发生的征兆和外在表现的思想,提出了一种基于变化危险感知的信号自适应提取方法,采用数值微分描述数据的变化,再从变化中提取输入信号.其次,原DC模型中异常抗原的评价方式对突变性故障能有效检测,却无法及时发现渐变性故障,提出了采用T细胞浓度作为故障评价指标.最后,在DAMADICS和TE两个基准平台上,将本文方法与原DCA算法和传统主元分析法(principal component analysis,PCA)进行比较测试.实验结果表明NDDC-FD方法不仅提高了原DCA算法的适应性,且和DCA、PCA相比具有较高检测率的同时,更能较早地检测到渐变性故障.因此,本文提出的故障检测方法NDDC-FD具有一般性,且性能良好.

Currently,the DCA (dendritic cell algorithm) relies heavily on artificial experience to define the input signals in fault detection of different types of equipment,which is lack of adaptability and completeness.To address this problem,we propose a dendritic cell fault detection model based on numerical differentiation--NDDC-FD.In first place,according to change is the symptom and outward expression of system which is in danger,an adaptive signal extraction method based on danger perception of system status change is proposed,which uses numerical differentiation to calculate the change to extract the input signals.Next,the anomaly antigen evaluation method of original DC model can effectively detect abrupt fault,but it can′t detect incipient fault in time.Therefore,the fault evaluation indicator based on concentration of T cells is proposed.Finally,our method is tested on DAMADICS and TE benchmark,and compared with DCA and PCA (principal component analysis).The results show that NDDC-FD method not only improves the adaptability of DCA,but also has higher detection rate than DCA and PCA,and has lower detection delay time in incipient fault detection.Overall,our method is generality and has well performance in the fault detection of industrial equipment.