Transmission line fault detection and classification using cross-correlation and k-nearest neighbor

2015 ◽  
Vol 19 (3) ◽  
pp. 183-189 ◽  
Author(s):  
Aritra Dasgupta ◽  
Sudipta Debnath ◽  
Arabinda Das
Keyword(s):  
Fault Detection ◽  
Transmission Line ◽  
Cross Correlation ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Fault Detection And Classification

scholarly journals Fault Detection of a Spherical Tank Using a Genetic Algorithm-Based Hybrid Feature Pool and k-Nearest Neighbor Algorithm

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 991 ◽  
Author(s):  
Md Hasan ◽  
Jong-Myon Kim
Keyword(s):  
Fault Detection ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Time Frequency ◽  
Reduction Techniques ◽  
Spherical Tank ◽  
Frequency Domains ◽  
Dimensionality Reduction Techniques ◽  
Linear Dimensionality Reduction ◽  
Ae Signals

Fault detection in metallic structures requires a detailed and discriminative feature pool creation mechanism to develop an effective condition monitoring system. Traditional fault detection methods incorporate handcrafted features either from the time, frequency or time-frequency domains. To explore the salient information provided by the acoustic emission (AE) signals, a hybrid of feature pool creation and an optimal features subset selection mechanism is proposed for crack detection in a spherical tank. The optimal hybrid feature pool creation process is composed of two major parts: (1) extraction of statistical features from time and frequency domains, as well as extraction of traditional features associated with the AE signals; and (2) genetic algorithm (GA)-based optimal features subset selection. The optimal features subset is then provided to the k-nearest neighbor (k-NN) classifier to distinguish between normal (NC) and crack conditions (CC). Experimental results show that the proposed approach yields an average 99.8% accuracy for heath state classification. To validate the effectiveness of the proposed approach, it is compared to conventional non-linear dimensionality reduction techniques, as well as those without feature selection schemes. Experimental results show that the proposed approach outperforms conventional non-linear dimensionality reduction techniques, achieving at least 2.55% higher classification accuracy.

scholarly journals Identifikasi Ciri Garis Telapak Tangan Berbasis Template Matching dan Metode K-Nearest Neighbor

MIND Journal ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 25-35
Author(s):  
Asep Nana Hermana ◽  
Irma Amelia Dewi ◽  
Irwan Susanto
Keyword(s):  
Template Matching ◽  
Cross Correlation ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Normalized Cross Correlation

Telapak tangan merupakan ciri unik yang dimiliki oleh manusia yang dapat digunakan pada sistem identifikasi. Proses template matching membutuhkan perhitungan pencocokan untuk menentukan bagian kecil gambar yang memiliki nilai terbesar dikarenakan semakin besar nilai maka tingkat kecocokan semakin tinggi. Sehingga untuk pencocokan dibutuhkan perhitungan normalized cross correlation dengan perhitungan konvolusi yang setiap bagian pixel akan dilakukan pencocokan, diawali dari pixel bagian pojok kiri atas hingga pojok kanan bawah dan akan mendapatkan nilai pencocokan terbesar.Setelah mendapat nilai terbesar dilakukan k-nearest neighbor yang merupakan pengelompokan berdasarkan jarak dan untuk menentukan jarak k digunakan perhitungan euclidien distance. Selanjutnya pengelompokan berdasarkan voting terbanyak yang dimulai dari nilai jarak ketetanggaan terkecil hingga terbesar. Tingkat akurasi pengujian dari 30 sampel telapak tangan didapatkan presentase sebesar 86,67% teridentifikasi benar dan 13,33% salah.

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