Multiple Data-Dependent Kernel Learning for Circuit Fault Diagnosis

An analog circuit fault diagnosis method based on multi- data correlation kernel is proposed, and the UCI data set is used to verify the effectiveness of the proposed method. Then, a fault diagnosis method structure of tolerance circuit based on SVM is proposed. Taking Sallen key filter circuit as an example, the specific steps of establishing an analog circuit fault diagnosis model, including fault injection, are introduced: circuit simulation, fault feature extraction, and design of SVM fault classifier based on multi-data correlation kernel. Then, the Sallen key filter circuit and leap frog filter circuit are selected as the diagnosis objects. The HSPICE software is used to inject the fault into the circuit under test and establish the fault simulation model, so as to obtain the circuit data under different circuit states, and the circuit samples are used to establish the fault classifier based on SVM. Finally, the effects of SVM + MK, SVM + DK, and SVM + MDK on the fault classifier diagnosis are compared. The experimental results show that the three methods used in this paper are better than the analog circuit fault diagnosis method based on standard SVM, and the proposed analog circuit fault diagnosis method based on multi-data correlation kernel is the best in terms of diagnosis effect. On this basis, the SVM + MDK algorithm is more effective The establishment time and diagnosis efficiency of the model are relatively good.