GSNR

AcronymDefinition
GSNRGeometric Signal-to-Noise Ratio
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3 shows the relationship between the detection probability and parameter c for the DEF detector for different values of [alpha], where [gamma] = 1, N = 100, GSNR = 0 dB, and [P.sub.f] = 0.1.
4 shows the detection probability versus parameter c or p for the DEF, GKED and FLOM detectors with the DC signal, where [alpha] = 2, [gamma] = 1, N = 200, GSNR = -2 dB and [P.sub.f] = 0.1.
5 shows the curves between the detection probability and parameter c or p of the DEF, GKED and FLOM detectors in non-fading channels with a Gaussian signal, where [alpha] = 2,1.5, [gamma] = 1, N = 100, GSNR = 0 dB and [P.sub.f] = 0.1.
7, where [alpha] = 2,1, [gamma] =1, N = 100 and GSNR = 0 dB.
8 shows the detection probability versus GSNR for the DEF, FLOM and GKED detectors in non-fading channels, where [alpha] = 1, [gamma] = 1, Pf = 0.1.
MSE Analysis under Different a and GSNR. In this simulation, v(n) is set as S[alpha]S distribution noise, [alpha] = 0.8, p = 1.2, GSNR =18 dB, and L = 32.
Figure 6 is MSE analysis under different GSNR; the experimental results show that MSEs employing the STFTTFF method vary from 8 dB to 80 dB when GSNR change from 26 dB to 14 dB, but MSEs employing the FLOSTFTTFF method are in the range of -20 dB to -40 dB.
S[alpha]S distribution noise ([alpha] = 0.8, GSNR = 15 dB) is added in the fault signals, which is assumed as the actual working environment background noise.
We compare the performance of the STFT-TFF and FLOSTFTTFF methods; the results show that the performance of the FLOSTFT-TFF method is better than the STFT-TFF method, which can effectively suppress S[alpha]S distribution noise and work in low GSNR. In practical applications, we analyze the fault features from time-frequency representation of the mechanical bearing fault signals employing FLOSTFT-TFR method, extract time-frequency region of the fault signals from [alpha] stable distribution noise employing FLOSTFT-TFF method, and reconstruct the original fault signal employing IFLOSTFT.
The larger the GSNR value is, the better the target enhancement effect of the difference image obtained from the background prediction is.
GSNR achieves good results among the improved anisotropy, reaching 10.75.
SSIM 1 2 3 4 5 6 Top-Hat 0.575 0.592 0.485 0.563 0.552 0.567 TDLMS [14] 0.694 0.518 0.507 0.652 0.546 0.539 NPB[15] 0.769 0.642 0.635 0.726 0.686 0.654 ABP [16] 0.969 0.925 0.889 0.958 0.942 0.937 IABP 0.979 0.935 0.932 0.968 0.956 0.943 SG [17] 0.949 0.879 0.753 0.936 0.929 0.913 FRA [18] 0.968 0.915 0.885 0.954 0.935 0.921 MoG [19] 0.957 0.893 0.873 0.942 0.923 0.915 Table 5: GSNR comparison between various background prediction methods.