Table 3.
Results and characteristics offered by the proposed work and previous methods.
| Work | Proposed Methods | Damage Level | Accuracy (%) |
|---|---|---|---|
| [9] | 1. Feature extraction is performed by using Homogeneity analysis 2. Gaussian probability density function is employed as classifier. |
HBRB, 1- and 2BRB | 99 |
| [10] | 1. Features extraction is performed by using MUSIC technique 2. Bayes method is employed as classifier. |
1- and 2BRB | 100 |
| [12] | 1. Features extraction is performed by using Wavelet and Hilbert transforms. 2. Linear discriminant technique is employed as classifier. |
1- and 2BRB | 100 |
| [23] | 1. Feature extraction is performed by using Fractal dimension 2. Fuzzy logic is employed as classifier. |
HBRB, 1- and 2BRB | 95 |
| [26] | 1. Features extraction is performed by using extended Kalman filter 2. MUSIC technique is employed as classifier. |
HBRB and 1BRB | 100 |
| [43] | 1. Wavelet transform is used to transform the measured signals to images. 2. A CNN is employed as features estimator and classifier. |
3BRB | 99 |
| [70] | 1. Features extraction is performed by using Wavelet transform. 2. Correlation Pearson is employed as classifier. |
HBRB, 1- and 2BRB | 95 |
| [71] | 1. Feature extraction is performed by using Hilbert transform. 2. Gaussian probability density function is employed as classifier. |
HBRB, 1- and 1½BRB | 99 |
| Proposed work | 1. Short time Fourier transform is used to transform the measured signals to images. 2. A CNN is employed as features estimator and classifier. |
HBRB, 1- and 2BRB | 100 |