A Statistical Approach in Time-Frequency Domain Reflectometry for Enhanced Fault Detection

2018 ◽  
Author(s):  
Gyeong Hwan Ji ◽  
Geon Seok Lee ◽  
Chun-Kwon Lee ◽  
Gu-Young Kwon ◽  
Yeong Ho Lee ◽  
...  
Keyword(s):  
Fault Detection ◽  
Frequency Domain ◽  
Statistical Approach ◽  
Time Frequency ◽  
Frequency Domain Reflectometry

scholarly journals Fault Detection in Multi-Core C&I Cable via Machine Learning Based Time-Frequency Domain Reflectometry

2019 ◽  
Vol 10 (1) ◽  
pp. 158 ◽  
Author(s):  
Chun-Kwon Lee ◽  
Seung Jin Chang
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Frequency Domain ◽  
Nuclear Power ◽  
Power Plants ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Noise Interference ◽  
Time Frequency ◽  
Frequency Domain Reflectometry

The integrity and functionality of the control and instrumentation (C&I) cable systems are essential when it comes to ensuring the reliability and safety of system operations, especially in vehicles or power plants. Whenever a fault occurs in a multi-core cable, it not only affects signals of the individual faulty line but inflicts the rest through crosstalk and noise interference. Thus, it is imperative that cable diagnostic technologies are eligible of detecting the fault and further differentiating the faulty line to prevent the original fault from jeopardizing the entire system operation. We propose here a diagnostic method which detects the presence and the location of a fault, and further differentiates the faulty line within the multi-core C&I cables using a machine learning algorithm based on the time-frequency domain reflectometry results. Neural networks and the hierarchy clustering algorithm are used for fault detection and the identification of the faulty line. The proposed clustering algorithm is verified via experiments with four possible fault scenarios using automotive wires and C&I cables for nuclear power plants. Hence, the proposed algorithm allows a fault in multi-core cables to be accurately detected and estimated when given the location and the reflection coefficient of a fault.

Research of Aircraft Cable Fault Detecting Method Based on Hilbert Huang Transformation

2011 ◽  
Vol 214 ◽  
pp. 138-143
Author(s):  
Tao Jing ◽  
Lu Zhang ◽  
Xu Dong Shi ◽  
Li Wen Wang
Keyword(s):  
Frequency Domain ◽  
Short Circuit ◽  
Reference Signal ◽  
Open Circuit ◽  
Time Domain Reflectometry ◽  
Transform Method ◽  
Hilbert Huang Transform ◽  
Intermittent Faults ◽  
Time Frequency ◽  
Frequency Domain Reflectometry

Aircraft cable fault diagnosing is considered to be most important for engineering maintenance. Several methods for cables testing have been developed, such as TDR, FDR and TFDR. Time Domain Reflectometry (TDR) relays much on impedance changes on the fault position, which is hard to using in detecting high resistance defects, intermittent defects; Time Frequency Domain Reflectometry (TFDR) method is used to locate intermittent faults, continuous faults and cross-connection faults aircraft wire, however, the algorithm of TFDR is complex. To the "Hard Fault"(short circuit and open circuit), the Hilbert-Huang Transform method is used in determining the optimal bandwidth of the incident reference signal and analyzing the phase and amplitude difference of superimposed signal which from the incident signal and the reflected signal on defects. To the "Fray Fault", Time and Frequency Domain Reflectometry method can be used with the signal processing method with Hilbert-Huang Transform. The experimental results indicate that this method effectively detect all types of aircraft cable fault, particularly for short lengths of cable.

Sign in / Sign up

Export Citation Format

Share Document