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 differentiationNDDC-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.