Improved particle swarm optimization diagnosis method and system of analog non-linear direct current circuit

Abstract

The invention, which relates to the technical field of the analog circuit fault diagnosis, provides an improved particle swarm optimization diagnosis method and system of an analog non-linear direct current circuit. With the method and system, a problem that the existing pattern-classification-based fault diagnosis method does not has the enough diagnosis capability for the unknown fault state and a multi-fault state of the analog circuit can be solved. The method comprises: establishing a fault diagnosis equation; calculating fitness values of all particles; calculating a unified inertia weight of all particles; calculating changes of the fitness values; determining a final value of the inertia weight of all particles at current iteration; updating speeds and positions of all particles in the particle swarm; and repeating all above-mentioned steps until method convergence is realized. Compared with the standard PSO algorithm, the method and system based on the technical scheme have the high fault diagnosis coverage rate and the fast fault diagnosis speed.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis of analog circuits, in particular to an improved particle swarm optimization diagnosis method and system for simulating nonlinear DC circuits. Background technique [0002] Analog circuit fault diagnosis refers to solving the physical position and parameters of fault components when the topological structure of the circuit network, the input excitation signal (the excitation signal can also be selected) and the response to the fault are known. In the analog circuit, faults can be divided into Soft faults and hard faults, in which soft faults are caused by the slow change of component parameters over time and exceed tolerances, and hard faults are caused by sudden large deviations of component parameters (such as open circuit and short circuit). [0003] Most of the existing soft fault diagnosis methods for analog circuits are based on pattern classification theory. Such diagnostic methods...

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Problems solved by technology

[0005] The purpose of the present invention is to provide an improved particle swarm optimization diagnosis method and system for simulating nonlinear DC circuits, so as to solve the problem that the current fault diagnosis method based on pattern classification is insufficient in diagnosing the unknown fault state and multiple fault states of the analog circuit. question

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