## 6 Conclusions  
In order to measure porcelain insulators used in transmission towers, equipment with high field applicability was developed utilizing an auto impact hammer and MEMS sensors. The developed equipment was used to measure string porcelain insulators used at 154 kV. Based on the frequency response function, the frequency range of interest was set, feature extraction was performed, and four ML algorithm types were applied to distinguish normal and damaged caps. The damage assessment model was developed by analyzing the correlation between features and materials and defects extracted from the frequency response data using 88 porcelain insulators. Results and conclusions can be summarized as follows:  
Figure 14: Reanalysis based on the location of the classified data: (**a**) selection of median and boundary and unusual data for each material; (**b**) comparison of PC1 and PC2 values of selected A1–3 and C1–4 data; (**c**) FRF comparison of median and boundary data from normal cristobalite data; (**d**) FRF comparison of median and unusual data from normal alumina data.  
* Field adaptable equipment was developed via the use of auto impact hammers and MEMS sensors to improve the convenience of measuring equipment and reduce weight.
* In the FRF test results, for damage to the cap, direct striking of the cap was more clearly distinguishable between normal and damaged caps than striking the porcelain section. Moreover, the accuracy of material and defect data classification was increased by setting the frequency region of interest from 4 kHz to 10 kHz for the FRF results of the caps.
* Four classification methods were used to set the post-distribution area of data through ML classification, and a model was developed to distinguish between normal and damaged cap data to an extent of 100%. Further, all models exhibited a high accuracy in classifying the material of normal data, and the bagging method had the best prediction ability in classifying the material of defect data.
* The distinction between normal and cap damage specimens was correct, but some data were found to exist within the distribution ranges of different classes, depending on the material. The reason for this is that porcelain insulators involve a manual process during manufacture, which may lead to manufacturing errors. Moreover, the FRF of the normal specimen present near the interface between the normal and the damaged area is like the FRF of the damaged cap specimen, and thus management of the porcelain insulator representing these waveforms is necessary. In the future, to develop a better predictive model, it would be necessary to precisely set the distribution area using various cap damage specimens according to the degree of damage.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments and analyzed the data; [PERSON] and [PERSON] contributed device/analysis tools; [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Korea Electric Power Corporation (KEPCO). This work was supported by Research Assistance Program (2020) in the Incheon National University.  
The authors declare no conflict of interest.