Application of a morphological non-sampling wavelet method to the online detection signal processing of coal mine wire rope

Tian Jie, Wang Hong-Yao, Sven G. Bilen, Wu Xinli, Meng Guo-Ying

Research output: Contribution to journalArticle

Abstract

The testing of wire rope is vital in ensuring personnel safety during coal mine production. At present, it has proven difficult to successfully pre-treat wire rope detection signals, leading to recognition errors and other issues. To this end, this paper details a proposed application of the morphological non-sampling wavelet method to the online detection signal processing of coal mine wire rope. Based on existing mathematical morphological theory, morphological filtering and morphological non-sampled wavelet construction methods, this paper constructs a morphological non-sampling wavelet method suitable for the online detection of the signal characteristics of mine wire rope. This method is then applied to signal preprocessing. The experimental results show that the developed method can effectively filter out noise such as baseline drift, that the signal-to-noise ratio (SNR) of the processed data is 39 dB > 30 dB and the elapsed time is 2 s. The SNRs obtained using the existing wavelet transform method and the morphological filtering method are 17 dB and 22-30 dB, respectively, with elapsed times of 1.99 s and 1.97 s, respectively. In this paper, the effective filtering of the signal is realised under the condition that the processing time of the signal preprocessing method shows no obvious increase.

Original languageEnglish (US)
Pages (from-to)521-527
Number of pages7
JournalInsight: Non-Destructive Testing and Condition Monitoring
Volume61
Issue number9
DOIs
StatePublished - Jan 1 2019

Fingerprint

Wire rope
Coal mines
Signal processing
Signal detection
Wavelet transforms
Signal to noise ratio
Personnel
Testing
Processing

All Science Journal Classification (ASJC) codes

  • Mechanics of Materials
  • Mechanical Engineering
  • Metals and Alloys
  • Materials Chemistry

Cite this

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title = "Application of a morphological non-sampling wavelet method to the online detection signal processing of coal mine wire rope",
abstract = "The testing of wire rope is vital in ensuring personnel safety during coal mine production. At present, it has proven difficult to successfully pre-treat wire rope detection signals, leading to recognition errors and other issues. To this end, this paper details a proposed application of the morphological non-sampling wavelet method to the online detection signal processing of coal mine wire rope. Based on existing mathematical morphological theory, morphological filtering and morphological non-sampled wavelet construction methods, this paper constructs a morphological non-sampling wavelet method suitable for the online detection of the signal characteristics of mine wire rope. This method is then applied to signal preprocessing. The experimental results show that the developed method can effectively filter out noise such as baseline drift, that the signal-to-noise ratio (SNR) of the processed data is 39 dB > 30 dB and the elapsed time is 2 s. The SNRs obtained using the existing wavelet transform method and the morphological filtering method are 17 dB and 22-30 dB, respectively, with elapsed times of 1.99 s and 1.97 s, respectively. In this paper, the effective filtering of the signal is realised under the condition that the processing time of the signal preprocessing method shows no obvious increase.",
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Application of a morphological non-sampling wavelet method to the online detection signal processing of coal mine wire rope. / Jie, Tian; Hong-Yao, Wang; Bilen, Sven G.; Xinli, Wu; Guo-Ying, Meng.

In: Insight: Non-Destructive Testing and Condition Monitoring, Vol. 61, No. 9, 01.01.2019, p. 521-527.

Research output: Contribution to journalArticle

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