A fault diagnosis method for broken bar of AC asynchronous motor rotor based on three-phase current

Abstract

A three-phase current-based fault diagnosis method for AC asynchronous motor rotor broken bars, synchronously collecting the three-phase current signals of the AC asynchronous motor stator; setting the number K of VMD decomposition according to the signal characteristics; changing the fixed moving steps of fireflies in the basic firefly algorithm GSO Long, based on the initial position of fireflies initialized by chaotic sequences, an improved chaotic variable step-size firefly algorithm CSVGSO was proposed, and the fitness function for analyzing current signals was determined; the VMD decomposition penalty parameter α was optimized by using CSVGSO; based on the global optimal fitness value corresponding The penalty parameter α and the decomposition number K perform VMD decomposition on the stator three-phase current to extract the modal component corresponding to the fundamental frequency of the current signal; determine the reference phase of the current signal based on Park transformation; substitute the obtained phase information into the LMS algorithm to extract the current The basic frequency component in the signal is adaptively filtered; the frequency spectrum analysis is performed on the filtered current signal to detect the fault characteristic frequency of the broken bar of the motor rotor; the invention has the advantages of high accuracy and strong robustness.

Description

technical field [0001] The invention relates to the technical field of mechanical fault diagnosis and monitoring, in particular to a method for diagnosing a broken bar fault of an AC asynchronous motor rotor based on three-phase current. Background technique [0002] In the early stage of AC asynchronous motor rotor broken bar fault, the fault characteristic frequency component of the current signal is weak, which is easily covered by the fundamental frequency component and difficult to identify. Therefore, the suppression method of the current fundamental frequency component has attracted the attention of many scholars. The principle of LMS adaptive filtering is simple, the calculation is convenient, and it can be used to filter out the fundamental frequency component. Filtering the fundamental frequency component belongs to single-component adaptive filtering, so constructing reference signals accurately is the key technology. As a new signal non-recursive adaptive decomp...

AI Technical Summary

Problems solved by technology

However, the basic firefly algorithm also has the following shortcomings. The initial sampling method of the firefly position may produce a large number of fireflies far away from the optimal solution and at the edge of the solution space, and may not be able to generate the optimal solution.

However, fireflies moving with a fixed step size will lead to slow convergence in the later stage of the algorithm, poor optimization and oscillation near the peak.

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