Abstract
A procedure for the enhancement of alternating current field measurement (ACFM) detection performance is proposed based on a multi-parameter synergy analysis (MPSA) algorithm. Firstly, to gain the maximised ACFM signal characteristics, wavelet base property matching is adopted to choose the favourable wavelet bases. To this aim, the following six base properties should be considered: orthogonality, compact support, symmetry, discrete wavelet transform (DWT), vanishing moment and regularity. It is found that the applicable wavelet bases are Haar, Daubechies (DbN), Symlets (SymN) and Coiflets (CoifN). Secondly, the MPSA method is applied to select the optimal mother wavelet candidates. The candidate with the largest MPSA index value is regarded as the optimum wavelet base. Finally, the proposed MPSA denoising strategy is demonstrated using an ACFM experiment. The results indicate that wavelets Db4 with decomposition level (DL)9 and Sym7 with DL8 are most appropriate for x- and z-axis ACFM signal denoising, respectively. The enhanced ACFM detection performance is experimentally verified and it is found that the signal-to-noise ratio (SNR) is increased by 33.8 dB and 26.7 dB for the x- and z-axis signal, respectively.
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