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Articles

Inverse identification of impact locations using multilayer perceptron with effective time-domain feature

, , , & ORCID Icon
Pages 443-461 | Received 24 Jun 2016, Accepted 03 Apr 2017, Published online: 20 Apr 2017

Figures & data

Figure 1. Discrepancy of PAT than MAT for two same-located impacts when (a) impact location 1 and sensor 4 (b) impact location 14 and sensor 5.

Figure 1. Discrepancy of PAT than MAT for two same-located impacts when (a) impact location 1 and sensor 4 (b) impact location 14 and sensor 5.

Figure 2. (a) Deviation of TC (at sensor 1) due to ambient noise for two separate impacts at location 3 (b) Non-variety of TC at sensors 10 and 12 for impact at location 3. (sensor’s radial distance difference from impact source = 4.5 cm)

Figure 2. (a) Deviation of TC (at sensor 1) due to ambient noise for two separate impacts at location 3 (b) Non-variety of TC at sensors 10 and 12 for impact at location 3. (sensor’s radial distance difference from impact source = 4.5 cm)

Figure 3. General topology of MLP.

Figure 3. General topology of MLP.

Figure 4. Experimental set-up for impact identification study.

Figure 4. Experimental set-up for impact identification study.

Figure 5. Arrangement of the sensors and impact locations.

Figure 5. Arrangement of the sensors and impact locations.

Figure 6. Impact localization and impact quantification scheme.

Figure 6. Impact localization and impact quantification scheme.

Figure 7. Structure and distribution of data in different stages.

Figure 7. Structure and distribution of data in different stages.

Figure 8. Schematic diagram of the experimental procedure.

Figure 8. Schematic diagram of the experimental procedure.

Table 1. The effect of hidden neurons with different features and subsamples.

Figure 9. Impact localization error (radial cm) vs. no. of test impacts using MAT.

Figure 9. Impact localization error (radial cm) vs. no. of test impacts using MAT.

Figure 10. Impact localization error (radial cm) vs. no. of test impacts using PAT.

Figure 10. Impact localization error (radial cm) vs. no. of test impacts using PAT.

Figure 11. Impact localization error (radial cm) vs. no. of test impacts using TC.

Figure 11. Impact localization error (radial cm) vs. no. of test impacts using TC.

Table 2. Mean approximation error at individual locations.

Figure 12. Performance histogram of the selected features for impact localization (subsample 4).

Figure 12. Performance histogram of the selected features for impact localization (subsample 4).

Table 3. Success rate percentage of the features for different subsamples.

Table 4. Impact localization accuracy of few similar earlier studies.

Table 5. Mean and maximum standard deviation for various features (when normalized).

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