Non-Destructive Detection of Pipe Line Cracks Using Ultra Wide Band Antenna with Machine Learning Algorithm

In this article, an Ultra-Wide Band (UWB) antenna for the pipeline crack detection process is pro-posed. A UWB antenna has been designed with the dimension of 32 x 32 mm2 and it resonates from 3 GHz to 10.8 GHz. The designed antenna produces a peak gain of 4.36 dB. A pair of UWB antennas are employed in various pipeline scenarios and the received pulse from antenna 1 to antenna 2 is used for further processing and detection of pipeline cracks. Through the suitable machine learning data classifier algorithm the dimen-sion of the crack has been detected. The various fea-tures such as mean, standard deviation (& sigma;), mean aver-age deviation (mad), skewness, and kurtosis have been extracted from the received pulse. Then the three differ-ent machine learning algorithms namely Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Na ϊve Bayse (NB) were trained and tested using extracted fea-tures, and the dimension of the void has been identified. Out of these three machine learning algorithms, kNN provides better accuracy and precision. It predicts the small cracks with 100% accuracy having a dimension as small as 1 mm width.