Ant colony algorithm-based self-adapting stochastic resonance system parameter selecting method

Abstract

The invention discloses an ant colony algorithm-based self-adapting stochastic resonance system parameter selecting method, which comprises the steps of: firstly, carrying out pretreatment on a signal to enable the signal to satisfy with the requirements of a stochastic resonance small parameter and selecting an ant colony algorithm with an appropriate category; then, setting initial parameters in the ant colony algorithm according to known conditions and requirements; and finally, carrying out parameter optimization of stochastic resonance by using the selected ant colony algorithm and guiding the direction of the ant colony algorithm according to characteristic values returned by stochastic resonance treating signals till optimal parameters for a stochastic resonance system are found in a self-adapting way. The ant colony algorithm-based self-adapting stochastic resonance system parameter selecting method has the advantages that the parameter optimization in a large-scale area can be realized by using smaller ant colonies, the problem that the application of stochastic resonance is restricted due to difficulty in selecting system parameters is solved and effective diagnosis for incipient failure of mechanical equipment can be realized.

Description

technical field [0001] The invention belongs to the field of early fault feature extraction of mechanical equipment, and relates to a stochastic resonance system parameter selection method based on an ant colony algorithm, which can adaptively select optimal system parameters and realize effective diagnosis of early faults of mechanical equipment. Background technique [0002] With the development of science and technology, electromechanical equipment plays an increasingly important role in social and economic life, and the trend of large-scale and complex equipment undoubtedly increases the probability of failure. The occurrence of a single failure often triggers a chain reaction , causing the entire equipment or even the entire production line to fail to operate normally, resulting in huge economic losses and even casualties. Therefore, the condition monitoring and fault diagnosis of electromechanical equipment, especially the diagnosis of early faults, has far-reaching so...

AI Technical Summary

Problems solved by technology

For stochastic resonance systems, the general adjustment method is to fix one parameter and adjust the other. This method is simple and easy to implement, but when the deviation of the first parameter selection is too large, even if the second parameter can reduce the error The obtained value is only relative to the optimal value, and there is still a

Images