Recognition method of the squeezing force of shearer dragging cable based on improved deep forest

The dragging cable of shearer is often subjected to external squeezing pressure during operation, which causes partial discharge of the cable insulation and affects the service life of the cable. The existing research focuses on the analysis of partial discharge law and severity, and cannot evaluate the magnitude of stress borne by ethylene propylene rubber insulated cables. This results in the inability to grasp the operating status of mining ethylene propylene rubber insulated cables. In order to solve the above problems, a method based on improved Stacking-deep forest (S-DF) is proposed for recognizing the squeezing force of shearer dragging cables. The partial discharge of shearers dragging cables under different squeezing pressures is measured through experiments. The variation law of partial discharge spectra, average discharge current, maximum discharge amount, and breakdown field strength with the applied squeezing pressure and voltage are analyzed. The statistical feature parameters of partial discharge are calculated. Based on statistical feature parameters, the S-DF model is used to recognize the magnitude of squeezing pressures. The S-DF model introduces Stacking ensemble algorithm in deep forest (DF) to improve recognition accuracy. The research results indicate that under different voltages, the maximum discharge capacity and average discharge current decrease with the increase of extrusion pressure. The breakdown field strength shows a trend of first increasing and then decreasing with the increase of squeezing pressure. When the squeezing pressure is greater than 2 000 N, the breakdown field strength is lower than that of the non squeezing one. The statistical feature parameters of partial discharge under different squeezing pressures can be used as discharge fingerprints. The S-DF model can accurately recognize the magnitude of squeezing pressure on cables, and the recognition rate is higher than other traditional classification algorithms.