Research On Analog Circuit Fault Diagnosis Methods Based On SVDD And Parameter Identification

Fault classification is the key of intelligent fault diagnosis methods of analog circuit.The study of it has great significance on improving the fault diagnosis accurancy, and ensuring the effectivity of the diagnosis methods. SVDD not only has few parameters, but also has high efficient and strong expansibility.It also has the potential to solve the problem of on-line fault diagnosis, and has broad prospects of application on analog circuit fault diagnosis. The traditional fault diagnosis methods of analog circuit, suach as parameter identify and fault verification, don't have good performance in some applicable, but these methods still have advantages that the intelligent fault diagnosis methods don't have.This paper proposes three kinds of analog circuit fault classification methods based on SVDD and two kinds of fault diagnosis methods based on parameter identification.(1) Analog circuit fault classification method based on SVDD with support vector pre-extracting. The number of training samples of SVDD will be very big when there are many failt patterns, then the time and space complexity will become very high.This paper proposes the definition of dispersion of sample.And the relation between dispersion of a sample and the possibility of support vector is analyzed in theory.Training samples are extracted according to this relation, by which the reduced training sets can be formed.Experiment results show that the proposed method can effectively reduce the number of training sets; also it has better performance than other support vector pre-extracting methods. Analog circuit fault classification methods based on SVDD with support vector pre-extracting based on dispersion can effectively reduce the time and space complexity of SVDD fault classifier.(2) Analog circuit fault classification method based on second mapping SVDD.Second mapping SVDD can get a more tight description boundary with high adaptability to the distribution of sample. Analog circuit fault classifier based on second mapping SVDD can effectively reduce the overlapping of fault pattern spaces, and improve the fault diagnosis accurancy. Experiment results show that the fault classifier of analog circuit based on second mapping SVDD has improved the performance of the fault classifier based on SVDD, and it can be widly used in anlalog circuit fault diagnosis.(3)Analog circuit fault classification method based on All Samples SVDD (AS-SVDD).SVDD is a one-class classification method,and its performance will be low in multi-class classification if the sample information of non-support vectors are ignored completely.A fault classification method called All Samples SVDD is proposed, which merge the sample information of non-support vectors into the fault classification rule.Experiment results of analog circuit fault diagnosis show that the accurancy of AS-SVDD is highly improved compared with normal fault classifier based on SVDD.And AS-SVDD is more robust, the parameter slection is more easy, diagnosis results is more reliable.(4) Analog circuit fault diagnose methods based on parameter identification that based on genetic algorithm, including the method based on system parameter identification and the fault verification method based on parameter identification.The first one locate the fault of analog circuit module and estimate the grade of the fault according to the relasionship between system parameter and circuit module based on the system parameters that identified by GA.The second method integrate the advantages of fault parameter identify method and fault verification method,it use GA to verify the set of fault components and identify the parameter of the fault components. Experiment results show that these two methods are effective, and they are suitable for the diagnosis of parameter faults.The research of this paper is funded by Chinese National Natural Science Foundation (60501022), Aeronautical Science Foundation of China (2009ZD52045), Postgraduates Research and Innovation Program of Jiangsu Province(CX10B₀98Z).