Comparative study of wavelet based techniques for electromagnetic noise evaluation and removal

Abstract

Signals acquired from an industrial environment with many sources of electromagnetic interferences may be polluted by white noise. Polluted data segments with many steady consecutive periods can be used (sometimes unsuccessful) for the estimation of a denoised period from the steady acquired data by using the mean signal method. For data segments with at least 4 periods, when only certain segments (shorter than a period) can be considered steady, hybrid algorithms can be used to automatically evaluate the power of noise and afterward to perform the noise removal by using wavelet thrashing trees. This paper deals with 2 additional denoising techniques. The 1-st one is based on the Wavelet Package Transform and allows for the separation of the noise components which pollute a data segment of at least one period. The second approached denoising technique is also addressing one period data segments and estimates firstly the power of noise by using the energies of the vectors of details from the first 2 levels of a tree used by decompositions with the Stationary Wavelet Transform. The estimated power of noise is afterward used to establish the number of levels in the wavelet thrashing trees. In this last stage, two wavelet mothers were used. Simulated and real test signals were used and performance comparisons were performed.

Comparison of commercial and original methods for denoising electrical waveforms with constant or linearly variable magnitudes

“…A denoising technique available in Matlab by means of the function wden can also be used [10], [11]. Previous studies of authors [8], [9] helped in establishing the values of the parameters of this function used in the approached operational context (artificial test signals with rich harmonic content and notches, acquired signals with 700 samples per period). These values are: soft trasholding technique, per-level reevaluation of the noise level in the wavelet tree and a Daubechy wavelet mother with a filter of length 28 (‚db14').…”