Analog circuit fault diagnosis and positioning method based on output response matrix characteristic analysis

Abstract

The invention discloses an analog circuit fault diagnosis and positioning method based on output response matrix characteristic analysis, and the method carries out the fault diagnosis of an analog circuit by adopting the characteristic spectrum radius and the maximum singular value of a matrix, and does not need to deeply discuss the internal characteristics of the circuit. Fault diagnosis can be carried out only by measuring the output response of the circuit; the fault can be diagnosed by comparing the difference between the fault-free output response matrix and the fault output response matrix; and by calculating the matrix spectrum radius and the maximum singular value of the disturbance matrix, the fault can be recognized, the effect is remarkable, the fault diagnosis rate of analog circuit fault diagnosis is as high as 100%, compared with artificial intelligence analog circuit fault diagnosis which can only be implemented through an algorithm, the method does not need a large number of sample sets at all, the time of analog circuit fault diagnosis can be saved, a new method is provided for fault diagnosis of the analog circuit, and the positioning problem of analog circuit fault diagnosis can be quickly and effectively solved.

Description

technical field [0001] The invention relates to the field of analog circuit fault diagnosis, in particular to analog circuit fault feature extraction and feature classification, and in particular to an analog circuit fault diagnosis and location method based on output response matrix characteristic analysis. Background technique [0002] In general, the change of circuit capacitance and resistance is called soft fault, and the device in the circuit is directly damaged or cannot be used as hard fault. The diagnosis of soft fault is more difficult than the diagnosis of hard fault. Since fault location and fault parameter identification are still challenging, mature analog circuit fault diagnosis techniques have not yet been formed. So far, in most of the tests, the mixed-signal circuits in the analog part are prone to problems, so the research on fault diagnosis of analog circuits is very important. Support Vector Machine (SVM for short) is a small-sample learning method with...

AI Technical Summary

Problems solved by technology

Traditional methods cannot solve the problem of high dimensionality of fault feature data and poor multi-classification performance when fault samples overlap seriously. Neural network methods can achieve fast fault detection when a large number of training samples are required.

The Local Mean Decomposition (LMD) approximate entropy algorithm is also a good feature extraction method for analog circuits. K-Nearest Neighbor (KNN) is a lazy algorithm with high precision, but it needs to choose Appropriate parameter K with long diagnostic time

Images