Study On Dynamic Detection And Fault Diagnosis Approaches For Mine Hoisting Wire Rope

As the key part of a mine hoisting,wire rope undertakes the transportation tasks of the personnel,coal,ore,equipment,materials,etc.The safe operation state of wire rope is receiving increasing attention by many coal mining enterprises.Due to hoisting wire rope is long term in the process of the cyclic loading conditions,broken wires is easily produced during the fatigue,wear and tear,etc.If the broken wires are not detected in time,which even cause the serious threats to the mine hoisting system.Therefore,based on the principle of magnetic flux leakage(MFL)detection,the research on the dynamic detection and fault diagnosis approaches has great significance for the online monitoring,running evaluation and residual life prediction of wire rope.This study includes the following contents:(1)Based on the in-depth analysis of the MFL detection principle of defected wire rope,the analytical models of the internal and external broken wires are built by studying the magnetic charge theory and the geometric mathematical model of defect shape.The axial component and the radial component of leakage magnetic field are confirmed by using the spatial vector decomposition of magnetic induction intensity.The formula deduction and numerical calculation proves that the leakage magnetic field intensity of the external wire is higher than the internal magnetic induction intensity caused by the internal broken wires,and the axial component is slightly smaller than the radial MFL component.The finite element analysis method is used to simulate the two-dimensional structural parameters of the permanent magnet excitation device and the wire rope.The influences of the parameters such as defect size,lift-off value,permanent magnet and armature on the MFL signal are studied.The difference between the excitation uniformity and the saturation effect of the single-excitation mode and the double-excitation mode on the wire rope is compared.Based on the law of MFL signal obtained by finite element analysis,it provides a reference for the selection of structural parameters of steel wire rope permanent magnet excitation device,and designs a ring permanent magnet excitation device of wire rope.(2)Design of wire rope testing platform and MFL signals Acquisition system is designed and developed,including the adjustable variable working condition testing platform,multi-stage Hall sensor annular MFL detection device,and multi-channel high speed data acquisition and processing system.The experiment platform of the adjustable and variable working conditions is established to provide diversified working conditions of wire rope.Based on the three-dimensional vector decomposition of magnetic induction intensity and the structure characteristic of wire rope,multi-stage annular MFL detection hardware circuit board is designed to collect the axial component,the radial component,the tangential component of MFL signal.The hardware system simultaneously realizes the collection of MFL signals with different lift-off values and spatial position distribution sensor.Multi-channel high-speed data acquisition and processing system is developed to achieve the quick acquisition,database storage,remote monitoring,etc.(3)The different defected types of wire rope,running speed,excitation distance,lift-off value are researched to reveal the influence rule of MFL signal.This paper compares the characteristics of axial,radial and tangential MFL signal components.It is clear that the axial and radial MFL signals are obviously easy to detect.The two-dimensional MFL signal is converted into a three-dimensional spatial distribution,which realizes the three-dimensional visualization of the MFL distribution,which provides a new choice for the identification of the damage position of wire rope.Aiming at the weak MFL signal of chrome-plated wire rope,based on front MFL detection,a wire rope machine vision identification method is proposed.The multi-seed self-search area growth method is used to obtain the surface damage area of wire rope to achieve the distinguishing purpose between the surface damage and the internal damage.The method achieves the identification of the percentage of the circumferential damage area of wire rope surface.(4)A novel feature recognition method of MFL signals of broken wire is proposed based on dual-tree complex wavelet transform(DT-CWT).DT-CWT method opens a new situation to identify the characteristics of MFL signal of broken wire.DT-CWT method provides a high degree of shift-invariance,multi-resolution,sparse representation.Based on DT-CWT method,the axial MFL signal and the radial MFL signal are denoised and detrended.In order to quickly acquire the time domain characteristics of MFL signal of broken wire rope,based on the wavelet modulus maximum and the adaptive windowed search window,the time domain feature point extraction algorithm is proposed in combination with DT-CWT,which realizes the continuous combination of MFL characteristic signal at the same damage location of wire rope.Peak,wave width,area,power spectrum entropy of axial and radial MFL signal are used to establish the MFL characteristic data set of different damage degree of wire rope.The projection pursuit evaluation model is used to evaluate the characteristic index of MFL characteristic data set.The main component analysis of MFL signal characteristics of different defects is established by constructing new characteristic index.This method achieves statistical classification of damage degree of wire rope.(5)In order to study the intelligent identification of different damage degree data set of wire rope,based on the theory research of the extreme learning machine(ELM),the improved directions of the ELM algorithm are analyzed and researched,and a novel variable step incremental ELM algorithm(VSI-ELM)is proposed in this paper.The slope fluctuates of the training accuracy curve of ELM algorithm is used to choose the number of hidden-layer nodes of ELM.According to the mechanism of the randomly generated parameters of input weight matrix and bias matrix,an new improved particle swarm optimize(IPSO)algorithm to optimize the parameters of ELM so that it can obtain the best training and testing accuracy.Finally,the improved particle swarm optimization algorithm-based extreme learning machi ne(IPSO-VSI-ELM)is applied to finish the quantitative classification and identification of damage degree of wire rope,and the accuracy of the recognition rate is up to 97.6%.