An Intelligent Recognition Method of a Short-Gap Arc in Aviation Cables Based on Feature Weight Enhancement

• Published in: IEEE sensors journal 2023-02, Vol.23 (4), p.1-1
• Main Authors: Jing, Tao, Huang, Deshun, Mi, Zhe, Yao, Lei, Liu, Xiaolin
• Format: Article
• Language: English
• Subjects: Algorithms; Aviation; Aviation cable; Connectors; Correlation coefficients; Decomposition; Eigenvectors; Electric cables; Electric connectors; Empirical analysis; Frequency domain analysis; Kurtosis; PCA; Principal components analysis; Reduction; Short-gap arc; Time domain analysis; Vibration analysis; Vibration measurement; Weight enhancement; Wiring harnesses
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2022.3232571

The cases of loose pin of electrical connectors and broken wire harnesses easily occur in aviation cables due to vibration and thus cause the short-gap arc fault. When the vibration frequency exceeds 30 Hz, the short-gap arc fast burning and extinguishing properties make the changes in time domain and frequency domain of fault signals weak, thus increasing the difficulty in the identification of such a fault. In this paper, an arc recognition method based on weight enhancement (WE) and principal component analysis (PCA) is proposed to solve the identification problem of the short-gap arc fault caused by its unapparent frequency-domain characteristics. In the proposed method, the currents under different conditions were measured and analyzed based on the considerations of different vibration conditions and loads. The measured current signals were firstly decomposed with the empirical modal decomposition (EMD) method and then optimal intrinsic mode functions (IMFs) were selected based on the correlation coefficient-kurtosis index to reconstruct the signals with weight enhancement algorithm. The time domain, frequency domain, and sample entropy characteristics extracted from reconstructed signals were used to establish the three-dimensional eigenvectors with direct fusion algorithm. Finally, the short-gap arc fault was identified with principal element analysis and dimension reduction method. The experimental results proved that weight enhancement could improve the selection of the optimal direction of PCA dimensional reduction projection and that the short-gap arc fault of aviation cables could be quickly and effectively identified with the proposed method.