Key Technique Of On-line Detection For Coal Mine-Hoist Wire Rope

This thesis investigates on on-line detection methods of coal mine-hoist wire rope flaws, whose background is the project"Network detection system of coal mine-hoist wire rope"supported by HUAIBEI MINING(GROUP) CO.LTD. In this study, magnetic flux leakage detection technologies are choosen as the method of detecting wire rope flaws aiming at the special application environments of coal mine-hoist wire rope. According to the characteristics, detection ability and defaults in present detection methods of wire rope, some theory, simulation and experimental study have been carried out.This study includes the following contents:(1) The spatial distribution analysis model of magnetic flux leakage signal for wire rope defaults is established, and the formative mechanism and leakage magnetic characteristic of magnetic leakage signal have been studied. The impact of geometrical dimension and lift-off value on magnetic flux leakage signal also have been studied, and obtained influence rule with an important reference value which provides a theoretical guidance for optimizing the design of wire rope flaw detection sensor. The spatial distribution analysis model of magnetic flux leakage signal was validation through experimental methods.(2) The FEM analysis model of magnetization loop structure is built. The impact relation curve of magnetization loop structure parameter on local magnetization effect is educed. And based on this, this study combines FEM and optimization algorithm to have an optimization design of main parameters of magnetization loop structure.(3) Based on the principle of magnetism, we proposed a detection method of multi-hall-element combined with array, which only needs 8 pieces of high sensitivity integrated hall element to implement all-sides detection about large diameter wire rope; what's more, it has good inhibitory effect to magnetic leakage from wire rope strands and background interference; what's the most, this method can detect two types of damage (LF and LMA) at the same time.(4) This study established an information processing model of wire rope magnetic flux leakage detection signal, set up an on-line detection experiment platform to detect wire rope damage. Meanwhile, based on the real magnetic flux leakage detection signal, this study proposed the method of selecting the optimal wavelet function and the optimal wavelet decomposition level through the signal correlation,then the results is given that the optimal wavelet basis function is sym6 , the optimal wavelet decomposition level is 4. The feasibility of this method is validated. According to the real magnetic flux leakage detection signal of wire rope, an adaptive wavelet thresholding de-noising method based on a novel thresholding function is proposed. The simulation results show that the method proposed in this paper,compared to the present wavelet threshold functions method, has higher signal to noise ratio and smaller minimum mean-square error, and has a better recognition effect on small singular signals produced by small damage.(5) The feature extraction of magnetic flux leakage signals about wire rope is finished. The quantitative identification using Improved Radial Basis Function neural network model for wire rope damage show that the accuracy of the recognition rate of wire rope damage is up to 96%.This method has higher recognition accuracy.