Audio fault detection for high-voltage cable terminations

Abstract

This paper addresses the challenge of weak discharge characteristics being masked by strong background noise in the acoustic detection of partial discharges in high-speed train high-voltage cable terminals. It innovatively introduces the Minimum Entropy Deconvolution (MED) method for fault diagnosis. Traditional detection methods are difficult to apply rapidly in vehicle maintenance due to equipment complexity and environmental constraints. The proposed method collects acoustic signals from cable terminals using portable devices and leverages the core advantage of the MED algorithm—designing an optimal inverse filter to maximize the enhancement of periodic impulse components within the signal. This effectively extracts the pulse sequences associated with partial discharges from heavy noise. Combined with a 2000 Hz high-pass filter to suppress low-frequency interference, the method clearly identifies discharge signals characterized by 50 Hz and its harmonics in both the time-domain waveform and frequency spectrum. Experimental and field verification demonstrate that this method can accurately distinguish faulty cables. Furthermore, an integrated graphical user interface analysis program was developed based on this approach, automating and streamlining the detection process. This provides a novel, low-cost, and efficient on-site solution for the operation and maintenance of high-voltage cables in rail transit.

Keywords

High-voltage cable termination / Partial discharge / Audio detection / Minimum entropy deconvolution method / GUI

References

[1] S. Tenbohlen, D. Denissov, S.M. Hoek, et al., Partial discharge measurement in the ultra high frequency (UHF) range, IEEE Trans. Dielectr. Electr. Insul. 15 (6) (2008) 1544-1552.

[2] M.D. Judd, L. Yang, I.B.B. Hunter, Partial discharge monitoring for power transformers using UHF sensors part 1: sensors and signal interpretation, IEEE Electr. Insul. Mag. 21 (2) (2005) 5-14.

[3] M.D. Judd, L. Yang, I.B.B. Hunter, Partial discharge monitoring for power transformers using UHF sensors part 2: field experience, IEEE Electr. Insul. Mag. 21 (3) (2005) 5-13.

[4] J.E. Timperley, A. Electric, P. Service, Power apparatus and systems, IEEE Transactions on Power Apparatus and System 102 PAS, 1983, pp. 693-698.

[5] R. Bartnikas, Partial discharges their mechanism, detection and measurement, IEEE Trans. Dielectr. Electr. Insul. 9 (5) (2002) 763-808.

[6] X.H. Xin, J.S. Li, H.Y. Ji, et al., Study of cross-shaped ultrasonic array sensor applied to partial discharge location in transformers, Proc. Chin. Soc. Electr. Eng. 33 (21) (2013) 154-162.

[7] Q.H. Xie, Y.Q. Li, F.C. Lv, et al., Method for PD location in oil combining ultrasonic phased array with wideband array signal processing, Proc. Chin. Soc. Electr. Eng. 29 (28) (2009) 13-17.

[8] W.K. Li, P. Sun, C.H. Liu, et al., Partial discharge defect detection and analysis for 110kV cable GIS terminals, High. Volt. Eng. 45 (2) (2019) 42-46.

[9] R.A. Wiggins, Minimum entropy deconvolution, Geoexploration 16 (1/2) (1978) 21-35.

[10] N. Sawalhi, R.B. Randall, H. Endo, The enhancement of fault detection and diagnosis in rollng element bearings using minimum entropy deconvolution combined with spectral kunosis, Mech. Syst. Signal Proc. 21 (2007) 2616-2633.

[11] N. Sawalhi, R.B. Randall, Spectral kurtosis enhancement using autoregressive models, A CAM Conference, Melbourne, 2005.

[12] H. Endo, R.B. Randal, Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter, Mech. Syst. Signal Proc. 21 (2007) 906-917.

[13] R.L. Jiang, Research on Rolling Bearing Fault Diagnosis based on MED, Shanghai Jiao Tong University, Shanghai, 2013.

[14] H. Wang, L. He, Wheelset bearing fault diagnosis using MED, China Meas. Test. 42 (1) (2016) 114-120.