Application of Augmented Spread Spectrum Time Domain Reflectometry for Detection and Localization of Soft Faults on a Coaxial Cable

• Published in: IEEE transactions on aerospace and electronic systems 2022-12, Vol.58 (6), p.4891-4901
• Main Authors: Shi, Xudong, Li, Ruipu, Zhang, Haotian, Zhao, Hongxu, Liu, Yang
• Format: Article
• Language: English
• Subjects: Amplitudes; Aviation; Bend radius; Cable fault detection; Coaxial cables; Decomposition; Fault detection; Fault location; Faults; Fourier transforms; Frequency distribution; Impedance; Insulation; Integrated circuit modeling; Kurtosis; Power cables; reassigned spectrogram; Reflectometry; Short circuits; soft faults; Spread spectrum; spread spectrum time domain reflectometry (SSTDR); Time domain analysis; Time-frequency analysis; variational mode decomposition (VMD); Vibration effects
• ISSN: 0018-9251; 1557-9603
• DOI: 10.1109/TAES.2022.3184913

Aviation cables are an essential part of aircraft, which transmits signals and power. Aviation cable faults often occur due to drastic changes in temperature and humidity and the effects of vibration. Obvious cable faults, such as open circuits and short circuits, can be detected during maintenance. However, some soft faults like the loose connection of the connector, damage of the insulation layer, and improper cable bending radius cannot be detected. Spread spectrum time domain reflectometry (SSTDR) is a diagnostic method for cable faults; however, the amplitude of the reflected signal is low when the soft fault occurs. SSTDR is only sensitive to apparent faults, but it is insufficient to detect soft faults. This article proposes an aviation cable fault detection and location method, augmented spread spectrum time domain reflectometry (ASSTDR), to detect soft faults effectively. First, the SSTDR results are decomposed into some intrinsic mode functions (IMFs) using the improved variational mode decomposition and selects the IMF with the highest kurtosis to analyze. Second, short-time Fourier transform (STFT) is applied to deal with the selected IMF to enhance the amplitude characteristics. Then, reassign spectrogram is computed to improve the resolution of STFT time-frequency distribution images to locate the location of soft faults. Finally, the effectiveness of ASSTDR is verified by experiments with different fault types and fault degrees. The comparative experiments demonstrate that the method can accurately detect soft faults.