Skip to main content
padlock icon - secure page this page is secure

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

Buy Article:

$22.00 + tax (Refund Policy)

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.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: COAL MINE WIRE ROPE; MORPHOLOGICAL NON-SAMPLING WAVELET; SIGNAL PROCESSING

Document Type: Research Article

Publication date: September 1, 2019

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more