TY - JOUR
T1 - Parameter analysis of pulsed eddy current sensor using principal component analysis
AU - Nafiah, Faris
AU - Tokhi, Mohammad Osman
AU - Shirkoohi, Gholamhossein
AU - Duan, Fang
AU - Zhao, Zhanfang
AU - Asfis, Giorgos
AU - Rudlin, John
PY - 2021/3/1
Y1 - 2021/3/1
N2 - Pulsed eddy current (PEC) technique provides a means to inspect structures without surface contact. It is particularly useful when the structure's surface is rough or inaccessible, such as insulated pipes in pipeline. Probe parameters of a PEC system, especially the sensing and excitation coil diameters, can significantly affect the PEC system's performance. Thus, detailed analysis of these parameters is paramount in developing a PEC system. Currently, this is accomplished by establishing the trend of features with respect to the analyzed variables, e.g. sample thicknesses. However, prior to extracting these features, a number of configuration parameters have to be determined. For this reason, analyzing PEC performance over a range of coil diameter values is rather time-consuming as both the sensing and excitation coil diameters significantly affect the received signals. Principal component analysis (PCA) is proposed as an alternative to the feature extraction. The work here analyzes the trends contributed by the PCA scores for different values of sensing and excitation coil parameters. Results from both numerical simulations and experiments suggest that the sensitivity of the PEC probe is highly correlated with the excitation coil diameter, while the excitation-sensing coil distance is not significant in determining the sensitivity of the PEC probe. These findings are consistent with those reported in the literature, suggesting the potential of adopting PCA for an automated PEC performance analysis process.
AB - Pulsed eddy current (PEC) technique provides a means to inspect structures without surface contact. It is particularly useful when the structure's surface is rough or inaccessible, such as insulated pipes in pipeline. Probe parameters of a PEC system, especially the sensing and excitation coil diameters, can significantly affect the PEC system's performance. Thus, detailed analysis of these parameters is paramount in developing a PEC system. Currently, this is accomplished by establishing the trend of features with respect to the analyzed variables, e.g. sample thicknesses. However, prior to extracting these features, a number of configuration parameters have to be determined. For this reason, analyzing PEC performance over a range of coil diameter values is rather time-consuming as both the sensing and excitation coil diameters significantly affect the received signals. Principal component analysis (PCA) is proposed as an alternative to the feature extraction. The work here analyzes the trends contributed by the PCA scores for different values of sensing and excitation coil parameters. Results from both numerical simulations and experiments suggest that the sensitivity of the PEC probe is highly correlated with the excitation coil diameter, while the excitation-sensing coil distance is not significant in determining the sensitivity of the PEC probe. These findings are consistent with those reported in the literature, suggesting the potential of adopting PCA for an automated PEC performance analysis process.
U2 - 10.1109/JSEN.2020.3036967
DO - 10.1109/JSEN.2020.3036967
M3 - Article
SN - 1530-437X
VL - 21
SP - 6897
EP - 6903
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 5
ER -