Abstract：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.
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2019, Vol. 47 
