Magnetic control submerged-arc welding seam tracking signal analyzing method based on experience wavelet transformation

Abstract

A magnetic control submerged-arc welding seam tracking signal analyzing method based on experience wavelet transformation mainly solves the problems that existing hardware filtering is not complete, low-frequency interference signals similar to seam tracking signals are difficult to filter out by a wavelet filtering method, and EMD unreasonable decomposition convergence conditions, over-envelope, under-envelope and the like are liable to cause modal aliasing. According to the key points of the technical scheme, a magnetic control submerged-arc welding arc sensor controls swinging of arcs so as to carry out welding seam groove scanning, a Hall sensor collects original signals and carries out hardware filtering processing on the original signals, the experience wavelet transformation signal analyzing method is utilized for carrying out low-frequency analyzing on the signals after the hardware filtering, one wavelet filter set is adaptively selected according to Fourier spectrum characteristics of the signals so as to extract different AM-FM components of the signals, then Hilbert conversion is carried out on each experience modal function, and meaningful instantaneous signal characteristics are obtained. By adopting the method, the more precise welding seam tracking signals are extracted, and the information of other low-frequency interference signals are obtained simultaneously.

Description

technical field [0001] The invention belongs to the field of welding automation technology for magnetron submerged arc welding arc sensor welding signal processing, in particular to a magnetron submerged arc welding seam tracking signal analysis method based on empirical wavelet transform. Background technique [0002] The welding arc of magnetron submerged arc welding is a typical nonlinear and non-stationary time-varying load. As the welding process progresses, the combustion conditions of the arc load and the closely related droplet transfer process will change. These changes will be reflected in the time domain and frequency domain of the electrical signal of the welding process. [0003] The analysis, processing and feature extraction of nonlinear and non-stationary signals have always been one of the hot issues in the academic and engineering circles. Common methods for studying non-stationary signals include windowed Fourier transform, continuous wavelet transform, a...

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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that the existing hardware filtering is not complete, the wavelet filtering method is difficult to filter out the low-frequency interference signal similar to the welding seam tracking signal, and the EMD decomposition convergence condition is unreasonable, and the over-envelope and under-envelope are prone to modal confusion. stack problem

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