Method for evaluating reliability life of ultra-deep vertical shaft hoisting steel wire rope

Abstract

The invention relates to the field of mine construction, in particular to an ultra-deep vertical shaft hoisting steel wire rope reliability life evaluation method which comprises a top event T, a middle event Ai and a basic event Xi. Establishing a finite element coupling simulation model of the multilayer winding dynamics of the hoisting steel wire rope according to the design parameters, and inputting different working condition parameters for simulation processing; monitoring characteristic data output by a high-definition industrial camera, a thermal infrared imager, a three-dimensional magnetic flux leakage detector, a temperature and humidity sensor and the like which are installed on the elevator and obtained simulation result data are input into a steel wire rope multi-source information database; based on a steel wire rope multi-source information database, a Birnbaum algorithm is adopted to calculate probability importance degrees of a fault tree top event T, a middle event Ai and a basic event Xi of the lifting steel wire rope in the unfavorable winding working condition, the accident occurrence accurate probability is output, and the lifting frequency of the lifting steel wire rope in the unfavorable winding state is determined under the given probability. And evaluating the reliability of the lifting steel wire rope in real time.

Description

Technical Field

[0001] The present invention relates to the field of mine construction, and particularly relates to a method for evaluating the reliability life of a hoisting wire rope for ultra-deep vertical shafts, which can quantitatively evaluate the reliability of the hoisting wire rope in real time. Background Art

[0002] With the increasing demand for various mineral resources in China, the number of mines and the mining depth are also increasing year by year. The single-drum hoisting system is an essential technical equipment during the construction of ultra-deep vertical shafts at the 2500m level. The single-drum hoisting system for ultra-deep vertical shafts includes a drum, a hoisting container, a head sheave, a speed reducer, a hoisting wire rope, etc. Among them, one end of the hoisting wire rope is wound around the drum in multiple layers, and the other end bypasses the head sheave installed on the headframe and is connected to the hoisting container. The hoisting and lowering of materials are realized by starting, stopping, accelerating, decelerating, and reversing the rotation of the driving drum. During the hoisting process of ultra-deep vertical shafts, the hoisting wire rope is wound around the drum in multiple layers. Factors such as the time-varying length, longitudinal-torsional coupling characteristics, load characteristics, and external excitation of the hoisting wire rope lead to the impact and vibration characteristics of the hoisting wire rope. Coupled with environmental factors such as temperature, humidity, and corrosive media, as well as factors such as the mismatch between the drum pitch and the wire rope diameter, unreasonable values of the rope exit angle and the helix angle, too small pre-tightening force, and poor lubrication conditions, it is easy to cause adverse winding conditions such as wire rope jamming, biting, and tangling on the drum, which will exacerbate the internal and external wear, wire breakage, and corrosion of the hoisting wire rope, and even lead to wire rope fracture accidents, thereby reducing the service and load-bearing safety reliability of the hoisting wire rope. Therefore, proposing a method for evaluating the reliability of the hoisting wire rope for ultra-deep vertical shafts has great practical significance for ensuring the service safety reliability of the hoisting system for ultra-deep vertical shafts at the 2500m level.

[0003] The currently existing evaluation methods for hoisting steel ropes include: Patent No. CN202110301508.5 discloses a steel rope safety evaluation method, which establishes an exponential model of the endurance strength and average breaking operation time of the steel rope based on the tensile force data, and can solve the failure probability of the steel rope at any operation time in different tensile force working environments. However, this method does not screen and extract features from the original data, and a large number of redundant signals will lead to slow signal transmission efficiency and increase the difficulty of signal processing; Patent No. CN202410194749.8 discloses a steel rope wireless flaw detection device and its damage evaluation method, which establishes a deep learning damage recognition model of the steel rope based on the magnetic flux leakage damage signal of the steel rope, and can quickly evaluate the damage state of the steel rope. However, the magnetic flux leakage signal is easily affected by vibration and electromagnetic interference, and it is difficult to accurately detect slight cracks and wear on the surface of the steel rope, resulting in distortion and loss of the collected signal; Patent No. CN202311515857.2 discloses an on-line monitoring system and method for steel ropes based on machine vision, which can evaluate the usage condition and remaining life of the steel rope by combining the temperature and humidity environmental impact factors and the surface image of the steel rope. However, the accuracy of this method is greatly affected by oil stains, light, etc. on the surface of the steel rope, and it cannot evaluate the internal damage of the steel rope. In addition, the existing evaluation methods for hoisting steel ropes only evaluate a certain parameter such as steel rope damage and breaking tensile force, and there is no reported reliability evaluation method for hoisting steel ropes that comprehensively considers the influence of drums, sheave wheels, environments, and hoisting parameters. Summary of the Invention

[0004] In order to overcome the deficiencies of the existing reliability evaluation methods for hoisting steel ropes, the present invention proposes a reliability life evaluation method for hoisting steel ropes in ultra-deep vertical shafts, which has the characteristics of comprehensiveness, real-time, quantification, intelligence, etc.

[0005] To achieve the above object, the present invention provides a reliability evaluation method for hoisting steel ropes in ultra-deep vertical shafts as follows: First, define a fault tree model for the adverse winding conditions of hoisting steel ropes, including the top event T, intermediate events Ai, and basic events Xi;

[0006] Then, establish a multi-layer winding AME dynamics-Abaqus finite element coupling simulation model for hoisting steel ropes according to the design parameters, input different working condition parameters for simulation processing, and then input the monitoring feature data output by high-definition industrial cameras, thermal infrared imagers, three-dimensional magnetic flux leakage detectors, temperature and humidity sensors, etc. installed on the hoist and the simulation result data into the steel rope multi-source information database;

[0007] Finally, based on the multi-source information database of the hoisting wire rope, the Birnbaum algorithm is used to calculate the probability importance of the top event T, intermediate event Ai, and basic event Xi of the fault tree for the adverse winding situation of the hoisting wire rope respectively, output the precise probability of the accident occurrence, determine the number of hoisting times when the hoisting wire rope does not have an adverse winding state under the given probability, and then evaluate the reliability of the hoisting wire rope in real time.

[0008] Furthermore, the top event T includes rope biting event, rope skipping event, and rope jamming event; the intermediate event A1 includes system control abnormality event, winch abnormality event, and operating condition abnormality event;

[0009] The system control abnormality includes signal distortion, immature signal representation method, electromagnetic interference, slow transmission rate, and high temperature and humidity;

[0010] The winch abnormality event includes abnormal drum structure and abnormal wire rope; among them, the abnormal drum structure includes rope groove pitch deviation, failure of the transition guiding device, and drum stall; the abnormal wire rope includes abnormal winding method, wire rope damage, and small wire rope pre-tightening force;

[0011] The operating condition abnormality includes large impact load, large rope deflection angle, poor lubrication condition, and large speed change.

[0012] Furthermore, the implementation steps of the reliability life assessment method for the hoisting wire rope in ultra-deep vertical shafts are specifically as follows:

[0013] a. Define the top event T, intermediate event Ai, and basic event Xi of the fault tree for the adverse winding condition of the hoisting wire rope, and establish a fault tree model for the adverse winding condition of the hoisting wire rope;

[0014] b. Input the drum and sheave structure parameters, hoisting wire rope parameters, and operating condition parameters to establish a multi-layer winding AME model of the hoisting wire rope, and output the rope deflection angle, wire rope tension difference on both sides of the sheave, hoisting load, vibration characteristic parameters of the suspension rope and sheave, and damage characteristic parameters of the wire rope and sheave;

[0015] c. Use Creo to establish an Abaqus simulation model of the drum, sheave, and wire rope in a parametric modeling manner, and set the material property parameters of the model, the contact parameters between the wire rope and the drum, the contact parameters between the wire rope and the sheave, as well as the tension difference on both sides of the sheave and the wrap angle at the sheave obtained from the AME model;

[0016] d. Change the drum parameters, rope exit form, and hoisting operating condition parameters to obtain simulation data under different conditions;

[0017] e. Monitor the noise reduction signal and the simulation signal based on multi-dimensional sensors, use the principal component analysis (PCA) method to reduce the eigenvalues, and establish a machine learning database based on the features after dimensionality reduction; extract the texture features, invariant moment features of the camera-captured images, and color moment features of the infrared images, use the kernel extreme learning machine (KELM) for decision-level fusion of neural networks and establish a deep learning feature database, and upload the dimensionality-reduced feature data and the image fusion features to the cloud network to form a multi-source information intelligent fusion database;

[0018] f. According to the fault tree model, use the Boolean algebra simplification method to simplify A i =(X a +X b ) or (X a .X b ) and substitute it into T = A 1 A 2 ...A i . The only simplest form single item K i =X a X b is obtained, and the prerequisite conditions for the system to have an adverse winding condition are clarified;

[0019] g. Based on the multi-source information fusion database and the fault tree model, statistically calculate the occurrence probabilities of each basic event. For the statistical parameters such as signal distortion and electromagnetic interference that are difficult to quantify, use the expert evaluation method to give the event probabilities. Finally, use the fuzzy triangular probability function μ A to normalize the basic event probabilities of the hoisting wire rope fault tree, and clarify the basic probabilities Pxi of each event in the hoisting wire rope adverse winding condition fault tree;

[0020] Among them, the fuzzy triangular probability function a l and a u are the upper and lower limits of the function respectively, and a M is the median value of the function. The function expression is:

[0021]

[0022] h. Substitute the probabilities Pxi of each basic event into Pki, and then use the disjoint sum of products algorithm (SDP) to calculate the real-time occurrence probability P T of the adverse winding condition of the single-wound hoisting wire rope. At the same time, calculate the probability importance Pi of different basic events, and explore the influence laws of the structural parameters, environmental parameters, and operating parameters of the drum on the adverse winding state of the hoisting wire rope in deep vertical shafts;

[0023] Among them, P T =P k1 +...P ki , P i =PT -P T(k) ;

[0024] i. According to the above steps, the number of hoisting times when the hoisting wire rope does not have an adverse winding state can be confirmed under a given probability, the reliability of the hoisting wire rope can be evaluated or predicted in real time, and the operation state of the hoisting wire rope can be adjusted in time or abnormal components can be repaired and replaced to ensure the safety and reliability of the hoisting wire rope.

[0025] Further, the above step b includes:

[0026] b-1 Establish a dynamic simulation model of the AME single-wound hoisting wire rope according to the design parameters, and input the simulation parameters of the hoisting system including the drum structure parameters, hoisting wire rope parameters, system control parameters, hoisting height, and hoisting load;

[0027] b-2 Solve the wire rope tension characteristics, load speed characteristics, load displacement characteristics, and wire rope vibration characteristic parameters under static equilibrium conditions;

[0028] b-3 Substitute the parameters solved by AME into the Abaqus finite element simulation model to obtain the wire rope fatigue damage characteristics at the drum and the head sheave;

[0029] b-4 Change different input parameters and repeat the operations of b1-b3 to obtain the simulation results under different working conditions of the multi-layer wound entity of the hoisting wire rope.

[0030] Further, in the above step d, the data collected by the sensor is first subjected to wavelet transform and compressive sensing noise reduction processing, and then the supplementary data under the same working conditions, the predicted data under different working conditions, and the noise-reduced data of the monitoring information are subjected to feature extraction and fusion processing and uploaded to the cloud network. The detailed steps of d are shown in d-1 to d-4:

[0031] d-1 Perform detrending, high-pass, and low-pass time-domain noise reduction processing on the signals such as speed, displacement, and mechanics collected by the sensor, and then perform Fourier transform on the signals to observe their phase and frequency domain characteristics. Perform filtering, dilation, erosion, and wavelet transform noise reduction processing on the collected infrared images and high-definition images;

[0032] d-2 Perform normalization, interpolation, segmentation, dimension elevation, and visualization processing on the single-dimensional signals after noise reduction processing;

[0033] d-3 Perform image processing such as normalization and grayscale conversion on the dimension-elevated visualization signals and image noise reduction signals;

[0034] d-4 Perform image fusion, feature fusion, and decision fusion on the processed signals, simulation prediction signals, and simulation supplementary signals in the above steps respectively, and then transmit the feature extraction data and fusion data to the cloud server to form a multi-source information intelligent database with real-time updates.

[0035] Furthermore, the step of clarifying the prerequisite conditions for the system to enter an adverse winding condition in step f above is as follows:

[0036] f-1 Calculate the minimal cut sets T of the fault tree

[0037] T = A 1 A 2 A 3

[0038] =(A 4 + X 1 )(A 5 A 6 )(X 2 X 3 X 4 X 5 )

[0039] =(X 1 + X 6 + X 7 + X 8 )(X 9 X 10 X 11 )(X 12 X 13 X 14 )(X 2 X 3 X 4 X 5 )

[0040] = X 1 X 2 X 3 X 4 X 5 X 9 X 10 X 11 X 12 X 13 X 14 + X 6 X 2 X 3 X 4 X 5 X 9 X 10 X 11 X 12 X 13 X 14 + X 7 X 2 X 3 X 4 X 5 X 9 X 10 X 11 X12 X 13 X 14 +X 8 X 2 X 3 X 4 X 5 X 9 X 10 X 11 X 12 X 13 X 14

[0041] K 1 ={X 1 ,X 2 ,X 3 ,X 4 ,X 5 ,X 9 ,X 10 ,X 11 ,X 12 ,X 13 ,X 14}

[0042] K 2 ={X 6 ,X 2 ,X 3 ,X 4 ,X 5 ,X 9 ,X 10 ,X 11 ,X 12 ,X 13 ,X 14}

[0043] K 3 ={X 7 ,X 2 ,X 3 ,X 4 ,X 5 ,X 9 ,X 10 ,X 11 ,X 12 ,X 13 ,X 14}

[0044] K 4 ={X 8 ,X 2 ,X 3 ,X 4 ,X 5 ,X 9 ,X 10 ,X 11 ,X 12 ,X13 , X 14};

[0045] f-2 Calculate the structural importance of the unfavorable winding conditions of the hoisting wire rope

[0046] (X 2 = X 3 = X 4 = X 5 = X 9 = X 10 = X 11 = X 12 = X 13 = X 14 ) > (X 1 = X 6 = X 7 = X 8 )

[0047] That is, the event importance ranking is obtained as: (Large rope groove pitch deviation = Transition guide device failure = Drum stall = Abnormal winding method = Small wire rope pre-tightening force = Wear and broken wires = Large impact load = Large rope deflection angle = Poor lubrication condition) > (Electromagnetic interference = Slow transmission rate = High temperature, humidity and large = Immature signal characterization method).

[0048] Furthermore, the steps of the disjunctive algorithm (SDP) in the above step h are as follows:

[0049] h-1 Disjunctive processing of the minimum cut sets:

[0050] K 1 = X 1 X 2 X 3 X 4 X 5 X 9 X 10 X 11 X 12 X 13 X 14

[0051] K 2 = X 6 X 2 X 3 X 4 X 5 X 9 X 10 X 11 X 12 X 13 X 14

[0052] K 3 = X 7 X 2 X3 X 4 X 5 X 9 X 10 X 11 X 12 X 13 X 14

[0053] K 4 = X 8 X 2 X 3 X 4 X 5 X 9 X 10 X 11 X 12 X 13 X 14 ;

[0054] h-2 calculation simplification processing:

[0055] Let X 2 X 3 X 4 X 5 X 9 X 10 X 11 X 12 X 13 X 14 = X t , then

[0056] K 1 = X 1 X t ; K 2 = X 6 X t ; K 3 = X 7 X t ;

[0057]

[0058] Beneficial effects:

[0059] 1. A complementary monitoring technology for wire rope damage that combines machine vision, thermal infrared sensing, and three-dimensional magnetic flux leakage detection is proposed, solving the problem of incomplete wire rope damage monitoring; characteristic data and simulation characteristic data of the multi-dimensional monitoring system for hoisting wire ropes are excavated, solving the problem of redundant monitoring signals; a neural network decision-making model based on fused characteristic data and an intelligent database of multi-source information for hoisting wire ropes are established, solving the problem of insufficient basic event data samples in the reliability assessment of hoisting wire ropes.

[0060] 2. A fault tree model for adverse winding conditions of hoisting steel ropes based on multi-source information is established, which can obtain the influence laws of drum structure parameters, winding methods, rope-out forms, etc. on the adverse winding conditions of hoisting steel ropes, obtain the prerequisite conditions for dangerous situations such as rope biting and rope skipping under different working conditions, the real-time reliability of hoisting steel ropes, and the predicted reliability of hoisting steel ropes, realizing the reliability assessment of hoisting steel ropes in 2500m-level ultra-deep vertical shafts. The assessment method has the advantages of comprehensiveness, real-time, intelligence, high precision, etc., and has wide practicability in the technical field. Description of the Drawings

[0061] Figure 1 It is the flow chart of the reliability assessment method for hoisting steel ropes in ultra-deep vertical shafts of the present invention;

[0062] Figure 2 It is the schematic diagram of the fault tree for adverse winding conditions of hoisting steel ropes in ultra-deep vertical shafts of the present invention;

[0063] Figure 3 It is the technical implementation plan for the reliability assessment of hoisting steel ropes in ultra-deep vertical shafts of the present invention;

[0064] Figure 4 It is the reliability assessment diagram for hoisting steel ropes in ultra-deep vertical shafts;

[0065] Figure 5 It is the table of the fault tree model for adverse winding conditions of hoisting steel ropes. Detailed Description of the Invention

[0066] The following further describes in detail the specific implementation manners of the present invention with reference to the drawings of the specification.

[0067] As Figures 1 to 5 shown, the reliability life assessment method for hoisting steel ropes in ultra-deep vertical shafts of the present invention includes a multi-dimensional monitoring system with sensor complementary technology, a single-winding hoist for deep vertical shafts, a multi-layer winding dynamics-finite element coupling simulation model for hoisting steel ropes, a multi-source information intelligent database, and a fault tree model for adverse winding faults of hoisting steel ropes based on the multi-source information intelligent database.

[0068] The hoisting steel rope hoist includes hoisting steel ropes, drums, sky wheels, hoisting loads, etc.

[0069] The multi-dimensional monitoring system includes Hall sensors for monitoring the rotational speeds of the monitoring sheave and the drum, and eddy current acceleration sensors for monitoring the vibration acceleration of the drum and the vibration acceleration of the sheave; it includes a high-definition industrial camera and a thermal infrared imager installed above the drum and the sheave, and a three-dimensional magnetic flux leakage detector at the rope outlet of the drum to achieve the monitoring of the damage morphology, damage location, and damage size of the drum, the sheave, and the wire rope; it includes a high-definition industrial camera installed on the roadway support to monitor the vibration acceleration and position of the vertical rope (the part of the wire rope passing over the sheave); it includes distributed temperature and humidity sensors installed at the drum, the sheave, and different positions in the roadway.

[0070] The dynamic - finite element coupling simulation model of the hoisting wire rope includes the multi-layer winding AME dynamic model of the hoisting wire rope and the Abaqus model of the multi-layer winding of the hoisting wire rope; through the coupling simulation model, data that is difficult to directly monitor in practice can be obtained, such as the wire rope tension difference on both sides of the sheave, the percentage of cross-section damage of the wire rope at the sheave, the rope deflection angle, and the hoisting load.

[0071] The sensor complementary monitoring technology monitors the same parameter of the wire rope using sensors with different physical characteristics, including using a high-definition industrial camera and a thermal infrared sensor to monitor the external damage of the wire rope and the drum, using a three-dimensional magnetic flux leakage sensor to detect the internal damage of the wire rope; using an eddy current acceleration sensor and an applied acceleration sensor to monitor the vibration acceleration of the drum spindle.

[0072] The simulation prediction data of different working condition parameters refers to changing control parameters such as the hoisting load, the drum rotational speed, the hoisting time, etc., and predicting parameters such as the size of the cross-section damage of the wire rope at the sheave, the vibration acceleration of the sheave, the vibration amplitude of the hanging rope, and the vibration acceleration.

[0073] The data signal feature extraction data includes time-domain features such as the peak value, peak-to-peak value, and wave width of the signal curve, frequency-domain features such as phase, amplitude, and frequency, and signal image features such as contour curve and gray value.

[0074] The multi-sensor fusion feature data includes the feature values of the signal curves of different sensors, the fusion feature values of the thermal infrared image and the high-definition camera image, and the fusion feature values of the magnetic flux leakage dimensionality-elevated signal and the image signal.

[0075] The multi-source intelligent database includes the extracted feature data of multi-dimensional sensors such as the drum rotational speed, the drum vibration acceleration, the sheave vibration acceleration, the temperature at different positions, the humidity at different positions, and the percentage of cross-section damage of the wire rope at different positions, includes the supplementary simulation feature data and simulation feature prediction data such as the tension difference on both sides of the sheave, the percentage of cross-section damage of the wire rope at the sheave, the rope deflection angle, and the hoisting load, and includes the data signal feature extraction data and the multi-sensor fusion data.

[0076] The reliability evaluation parameters of the hoisting steel wire rope include the influence laws of pitch, drum diameter, width, rope exit angle, rope exit position, hoisting load, hoisting speed, drum rotation speed, hoisting distance, etc. on the reliability of the hoisting steel wire rope, including the prerequisite conditions for the occurrence of adverse winding conditions of the hoisting steel wire rope.

[0077] The fault tree model of the adverse winding condition of the hoisting steel wire rope includes the top event T of the adverse winding condition of the hoisting steel wire rope, intermediate events Ai (such as abnormal system control, abnormal steel wire rope, abnormal drum, etc.), and various basic events Xi (such as large rope groove pitch deviation, failure of the transition guiding device, drum vibration, abnormal winding method, small pre-tightening force of the steel wire rope, etc.) that affect the running state of the hoisting steel wire rope. The details are shown in the following table;

[0078] Using the above-mentioned reliability evaluation method for the hoisting steel wire rope in ultra-deep vertical shafts, it includes the following steps:

[0079] a. Define the top event T, intermediate events Ai, and basic events Xi of the fault tree of the adverse winding condition of the hoisting steel wire rope, and establish a fault tree model of the adverse winding condition of the single-rope winding hoisting steel wire rope during the construction of ultra-deep vertical shafts;

[0080] b. Input the structural parameters of the drum and the sheave (mass, diameter, moment of inertia), the parameters of the hoisting steel wire rope (elastic modulus, stiffness, breaking tensile force), and the working condition parameters (drum rotation speed, hoisting height, hoisting load) to establish a multi-layer winding AME model of the hoisting steel wire rope, and output the rope deflection angle, the tension difference of the steel wire rope on both sides of the sheave, the hoisting load, and the vibration characteristics of the suspension rope and the sheave (vibration acceleration, vibration amplitude, vibration frequency);

[0081] c. Use Creo to establish an Abaqus simulation model of the drum, the sheave, and the steel wire rope in a parametric modeling manner, and set the material property parameters of the model, the contact parameters between the steel wire rope and the drum, the contact parameters between the steel wire rope and the sheave, as well as the tension difference on both sides of the sheave and the wrap angle at the sheave obtained from the AME model;

[0082] d. Change the drum parameters (mass, diameter, moment of inertia, pitch, groove depth), the rope exit form (rope exit angle, rope exit position), and the hoisting working condition parameters (drum rotation speed, hoisting height, hoisting load) to obtain simulation data under different conditions (rope deflection angle, tension difference of the steel wire rope on both sides of the sheave, hoisting load, vibration characteristics of the suspension rope and the sheave, vibration characteristics of the drum, and percentage of cross-section damage of the steel wire rope at different positions);

[0083] e. Monitor the noise reduction signal and the simulation signal based on multi-dimensional sensors, use the principal component analysis (PCA) method for eigenvalue dimensionality reduction, and establish a machine learning database based on the reduced features Fi; extract the texture features, invariant moment features of the camera-captured images, and color moment features of the infrared images, use the kernel extreme learning machine (KELM) for decision-level fusion of neural networks and establish a deep learning feature database Fii, and upload the reduced feature data and the image fusion features to the cloud network to form a multi-source information intelligent fusion database;

[0084] f. According to the fault tree model, use the Boolean algebra simplification method to substitute, and find the simplest form single item of only Xi, and clarify the prerequisite conditions for the system to have an adverse winding condition;

[0085] g. Based on the multi-source information fusion database and the fault tree model, statistically calculate the occurrence probability of each basic event. For the statistical parameters such as signal distortion and electromagnetic interference that are difficult to quantify, use the expert evaluation method to give the event probability. Finally, use the fuzzy triangular probability function to normalize the basic event probability of the hoisting wire rope fault tree, and clarify the basic probability Pxi of each event of the hoisting wire rope adverse winding condition fault tree;

[0086] Among them, the fuzzy triangular probability function a l and a u are the upper and lower limits of the function respectively, and is the median value of the function. The function expression is:

[0087]

[0088] h. Substitute the probability Pxi of each basic event into Pki, and then calculate the real-time occurrence probability of the adverse winding condition of the hoisting wire rope. At the same time, calculate the probability importance Pi of different basic events, and explore the influence law of the structural parameters, environmental parameters and operating parameters of the drum on the adverse winding state of the hoisting wire rope in deep vertical shafts;

[0089] Among them, P T =P k1 +...P ki , P i =P T -P T(k) ;

[0090] i. According to the above steps, it can be confirmed the number of hoisting times when the hoisting wire rope does not have an adverse winding state under a given probability, can evaluate or predict the reliability of the hoisting wire rope in real time, and timely adjust the operating state of the hoisting wire rope or repair and replace abnormal components to ensure the safety and reliability of the hoisting wire rope.

[0091] Fault tree model for adverse winding conditions of hoisting steel ropes in deep vertical shafts, including top event T - adverse winding conditions such as rope biting, rope skipping, and rope jamming, intermediate event A1 - abnormal system control, intermediate event A2 - abnormal winch, and intermediate event A3 - abnormal operating conditions.

[0092] Intermediate event A1 - abnormal system control includes intermediate event A4 - signal distortion, basic event X1 - immature signal representation method, basic event X6 - electromagnetic interference, X7 - slow transmission rate, and X8 - high temperature and humidity.

[0093] Intermediate event A2 includes intermediate events A5 - abnormal drum structure and A6 - abnormal steel rope. Among them, intermediate event A5 - abnormal drum structure includes basic events X9 - rope groove pitch deviation, X10 - failure of transition guiding device, and X11 - drum stall; intermediate event A6 - abnormal steel rope includes basic events X12 - abnormal winding method, X13 - steel rope damage, and X14 - small pre - tightening force of steel rope; the above - mentioned intermediate event A3 - abnormal operating conditions includes basic events X2 - large impact load, X3 - large rope deviation angle, X4 - poor lubrication condition, and X5 - large speed change.

[0094] The reliability evaluation method for hoisting steel ropes in ultra - deep vertical shafts is used as follows:

[0095] a. Define the top event T, intermediate events Ai, and basic events Xi of the fault tree for adverse winding conditions of hoisting steel ropes, and establish a fault tree model for adverse winding conditions of single - rope winding hoisting steel ropes during the construction of ultra - deep vertical shafts;

[0096] b. Establish an AME dynamic simulation model and an Abaqus finite - element simulation model according to the operating conditions of the ultra - deep vertical shaft hoisting system. The input parameters are three - dimensional model structure parameters, material parameters, and hoisting operating conditions parameters, and the output parameters are hoisting steel rope characteristic parameters (speed, acceleration, rope deviation angle, tension, amplitude, frequency), and steel rope and sheave damage characteristic parameters (damage location, damage size);

[0097] In actual operation, step b is specifically executed as shown in steps b - 1 to b - 4;

[0098] b - 1. Establish an AME single - winding hoisting steel rope dynamic simulation model according to the design parameters. The input hoisting system simulation parameters include drum structure parameters (mass, diameter, moment of inertia), hoisting steel rope parameters (elastic modulus, stiffness, breaking force), system control parameters (drum speed, hoisting load), and hoisting height, hoisting load, etc.;

[0099] b - 2. Solve parameters such as steel rope tension characteristics, load speed characteristics, load displacement characteristics, and steel rope vibration characteristics under static equilibrium conditions;

[0100] b-3 Substitute the parameters obtained from the AME solution into the Abaqus finite element simulation model to obtain the fatigue damage characteristics of the steel wire rope at the drum and the crown block.

[0101] b-4 Repeat the operations in b1 - b3 with different input parameters to obtain the simulation results under different working conditions of the multi-layer winding entity of the hoisting steel wire rope.

[0102] c. Based on the multi-dimensional monitoring system, collect the monitoring parameters of the physical characteristics of the hoisting steel wire rope, such as the speed, acceleration, tension, vibration, etc. of the hoisting steel wire rope, and obtain the damage conditions of the steel wire rope and the drum structure through high-definition industrial cameras, infrared sensors, and electromagnetic sensors, etc.

[0103] d. Perform noise reduction processing on the data collected by the sensors, such as wavelet transform and compressive sensing, and then perform feature extraction and fusion processing on the supplementary data under the same working conditions, the predicted data under different working conditions, and the noise-reduced data of the monitoring information, and upload them to the cloud network. The detailed steps of d are shown in d-1 to d-4.

[0104] d-1 Perform time-domain noise reduction processing on the signals such as speed, displacement, and mechanics collected by the sensors, such as detrending, high-pass, and low-pass filtering, and then perform Fourier transform on the signals to observe their frequency-domain characteristics such as phase and frequency. Perform noise reduction processing on the collected infrared images and high-definition images, such as filtering, dilation, erosion, and wavelet transform.

[0105] d-2 Perform normalization, interpolation, segmentation, dimension elevation, and visualization processing on the single-dimensional signals after noise reduction processing.

[0106] d-3 Perform image processing such as normalization and gray-scale conversion on the visualized signals after dimension elevation and the image noise-reduced signals.

[0107] d-4 Perform image fusion (wavelet transform method, Kalman filter algorithm), feature fusion (neural network, nearest neighbor clustering method), and decision fusion (kernel function vector product) on the processed signals, simulation prediction signals, and simulation supplementary signals in the above steps respectively, and then transmit the feature extraction data and fusion data to the cloud server to form a multi-source information intelligent database with real-time updates.

[0108] e. Analyze the characteristics of the adverse winding conditions of the hoisting steel wire rope. Based on the fault tree analysis method, clarify the top event, intermediate events, and basic events of the fault tree for the adverse winding conditions of the hoisting steel wire rope, and establish a fault tree model for the adverse winding conditions of the hoisting steel wire rope in ultra-deep vertical shafts.

[0109] f. According to the fault tree model for the adverse winding conditions of the hoisting steel wire rope and combined with the Boolean algebra method, calculate the minimum cut sets of the fault tree, and clarify the prerequisite conditions (structural importance) for the adverse winding conditions of the hoisting steel wire rope. The detailed steps are shown in f-1 to f-2.

[0110] f-1 Calculate the minimal cut sets of the fault tree and clarify the prerequisite conditions for the adverse winding conditions of the hoisting wire rope:

[0111] T = A 1 A 2 A 3

[0112] = (A 4 + X 1 )(A 5 A 6 )(X 2 X 3 X 4 X 5 )

[0113] = (X 1 + X 6 + X 7 + X 8 )(X 9 X 10 X 11 )(X 12 X 13 X 14 )(X 2 X 3 X 4 X 5 )

[0114] = X 1 X 2 X 3 X 4 X 5 X 9 X 10 X 11 X 12 X 13 X 14 + X 6 X 2 X 3 X 4 X 5 X 9 X 10 X 11 X 12 X 13 X 14 + X 7 X 2 X 3 X 4 X 5 X 9 X 10 X 11 X 12 X 13 X 14 + X 8 X2 X 3 X 4 X 5 X 9 X 10 X 11 X 12 X 13 X 14

[0115] K 1 ={X 1 ,X 2 ,X 3 ,X 4 ,X 5 ,X 9 ,X 10 ,X 11 ,X 12 ,X 13 ,X 14}

[0116] K 2 ={X 6 ,X 2 ,X 3 ,X 4 ,X 5 ,X 9 ,X 10 ,X 11 ,X 12 ,X 13 ,X 14}

[0117] K 3 ={X 7 ,X 2 ,X 3 ,X 4 ,X 5 ,X 9 ,X 10 ,X 11 ,X 12 ,X 13 ,X 14}

[0118] K 4 ={X 8 ,X 2 ,X 3 ,X 4 ,X 5 ,X 9 ,X 10 ,X 11 ,X 12 ,X 13 ,X 14}

[0119] Enter step f-2 to calculate the structural importance of the unfavorable winding condition of the hoisting wire rope:

[0120] (X 2 = X 3 = X 4 = X 5 = X 9 = X 10 = X 11 = X 12 = X 13 = X 14 ) > (X 1 = X 6 = X 7 = X 8 )

[0121] That is, the order of event importance is: (Large rope groove pitch deviation = Failure of transition guiding device = Drum stall = Abnormal winding method = Small wire rope pre-tightening force = Wear and broken wires = Large impact load = Large rope deflection angle = Poor lubrication condition) > (Electromagnetic interference = Slow transmission rate = High temperature, humidity and large = Immature signal characterization method);

[0122] g. Since the basic events under the unfavorable winding condition of the hoisting wire rope are independent of each other, but there are intersections between the minimum cut sets obtained, the disjoint algorithm is used to calculate the probability of the unfavorable winding condition of the hoisting wire rope. The detailed steps of g are shown in g-1 to g-2;

[0123] g-1 Disjointing of minimum cut sets:

[0124] K 1 = X 1 X 2 X 3 X 4 X 5 X 9 X 10 X 11 X 12 X 13 X 14

[0125] K 2 = X 6 X 2 X 3 X 4 X 5 X 9 X 10 X 11 X 12 X 13 X 14

[0126] K 3 = X 7 X 2 X3 X 4 X 5 X 9 X 10 X 11 X 12 X 13 X 14

[0127] K 4 = X 8 X 2 X 3 X 4 X 5 X 9 X 10 X 11 X 12 X 13 X 14

[0128] Simplify the g-2 calculation. Let X 2 X 3 X 4 X 5 X 9 X 10 X 11 X 12 X 13 X 14 = X t , then

[0129] K 1 = X 1 X t ; K 2 = X 6 X t ; K 3 = X 7 X t ;

[0130]

[0131] h. The accurate accident occurrence probability of some basic events can be obtained through the multi-source information intelligent database (such as wire rope wear and broken wires, large impact load, large rope deviation angle, etc.). For environmental factors such as high temperature and humidity, electromagnetic interference, and slow transmission efficiency that are difficult to quantitatively count, the fuzzy probability of basic events is given by using the expert evaluation and triangular fuzzy function analysis methods; substituting the above basic event probability Pxi into the minimum cut set Pki, the occurrence probability PT of adverse winding conditions such as rope biting and rope skipping can be determined;

[0132] P T = P K1 + P K2 + P K3 + P K4

[0133] i. Use the Birnbaum algorithm to calculate the probability importance of each basic event (the probability of the top event PTk after removing this event) respectively, and explore the influence law of each basic event on the adverse winding state of the hoisting wire rope in deep vertical shafts;

[0134] j. According to the above steps, the operating state and safety reliability of the hoisting wire rope in ultra-deep vertical shafts can be evaluated in real time, and the operating state of the hoisting wire rope can be adjusted in time, or abnormal components can be repaired or replaced to ensure the reliability of the hoisting wire rope.

[0135] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A reliability assessment method for hoisting wire ropes in ultra-deep shafts of mines, characterized by: Firstly, the fault tree model of the adverse winding condition of the hoisting wire rope is defined, including the top event T, the intermediate event Ai and the basic event Xi; Then, the AME dynamics-Abaqus finite element coupling simulation model of multi-layer winding of hoisting wire rope is established according to the design parameters, and different working condition parameters are input for simulation processing. Then, the monitoring feature data and simulation result data output by the high-definition industrial camera, thermal infrared imager, three-dimensional magnetic flux leakage detector and temperature and humidity sensor installed on the hoist are input into the wire rope multi-source information database; Finally, based on the wire rope multi-source information database and the fault tree model of the lifting wire rope adverse winding condition, the Birnbaum algorithm is used to calculate the probability importance of the top event T, the intermediate event Ai and the basic event Xi respectively, and the precise probability of the accident is output. Under the given probability, the number of hoisting times without the adverse winding of the lifting wire rope is determined, and the reliability of the lifting wire rope is evaluated in real time.

2. The reliability assessment method for ultra-deep shaft hoisting wire rope in a mine according to claim 1 is characterized in that: The top event T includes a rope biting event, a rope jumping event and a rope trap event; the intermediate event A1 includes a system control abnormality event, a winch abnormality event and an operating condition abnormality event; The system control anomalies include signal distortion, immature signal characterization methods, electromagnetic interference, slow transmission rate, and high temperature and humidity; The winch abnormal events include drum structure abnormalities and wire rope abnormalities; drum structure abnormalities include rope groove pitch deviation, transition guide device failure, and drum stall; wire rope abnormalities include abnormal winding mode, wire rope damage, and low wire rope preload; The abnormal operating conditions include large impact loads, large rope deflection angles, poor lubrication conditions, and large speed changes.

3. The reliability life assessment method for ultra-deep shaft hoisting wire rope according to claim 1 is characterized in that: The implementation steps are as follows: a. Clearly define the top event T, intermediate event Ai and basic event Xi of the fault tree of the unfavorable winding condition of the lifting wire rope, and establish the fault tree model of the unfavorable winding condition of the lifting wire rope. b. Input the design parameters and working condition parameters of the drum, sheave, wire rope, etc. to establish the multi-layer winding AME model of the hoisting wire rope, and output the rope deflection angle, the tension difference of the wire ropes on both sides of the sheave, the lifting load, the vibration characteristic parameters of the suspension rope and the sheave, and the damage characteristic parameters of the wire rope and the sheave; c. Use Creo to establish the Abaqus simulation model of the drum, sheave and wire rope by parametric modeling, and set the material property parameters of the model, the contact parameters between the wire rope and the drum, the contact parameters between the wire rope and the sheave, and the tension difference on both sides of the sheave and the wrap angle at the sheave obtained by the AME model; d. Change the drum parameters, rope output form and lifting working condition parameters to obtain simulation data under different working condition parameters; e. Based on multi-dimensional sensor monitoring of noise reduction signals and simulation signals, principal component analysis (PCA) is used to reduce the dimension of feature values, and a machine learning database is established based on the features after dimension reduction; texture features, invariant moment features of camera images and color moment features of infrared images are extracted, and a kernel extreme learning machine (KELM) is used to perform decision-level fusion of neural networks and establish a deep learning feature database, and the dimension reduction feature data and image fusion features are uploaded to the cloud network to form a multi-source information intelligent fusion database; f. According to the fault tree model, use Boolean algebra simplification method to simplify A i =(X a +X b )or(X a .X b )Substitute T = A1A2...A i , what we need is only the simplest form of Xi single term K i =X a X b , clearly define the prerequisites for the occurrence of adverse winding conditions in the lifting system; g. Based on the multi-source information fusion database and fault tree model, the probability of occurrence of each basic event is statistically analyzed. For parameters that are difficult to quantify, such as signal distortion and electromagnetic interference, the event probability is given by expert evaluation method. Finally, the fuzzy triangular probability function μ is used. A Normalize the basic event probability of the hoisting wire rope fault tree and clarify the basic probability Pxi of each event in the fault tree of the hoisting wire rope adverse winding condition; Among them, the fuzzy triangular probability function a l with a u are the upper and lower limits of the function, a M is the function median, and the function expression is: h. Substitute each basic event probability Pxi into Pki, and then use the non-intersection algorithm (SDP) to calculate the real-time probability P of the adverse winding condition of the single winding hoist wire rope T , and calculate the probability importance Pi of different basic events at the same time, and explore the influence of the structural parameters, environmental parameters and operating parameters of the drum on the adverse winding state of the deep shaft hoisting wire rope; Among them, P T =P k1 +...P ki , P i＝ P T -P T(k) ; i. According to the above steps, the number of times the lifting wire rope is lifted without an adverse winding state under a given probability can be confirmed, the reliability of the lifting wire rope can be evaluated or predicted in real time, and guidance can be provided for timely adjustment of the operating status of the lifting wire rope or maintenance and replacement of abnormal parts to ensure the safety and reliability of the lifting wire rope.

4. The reliability life assessment method for ultra-deep shaft hoisting wire rope according to claim 3 is characterized in that: Step b includes: b-1 Establish the AME single-wound hoisting wire rope dynamics simulation model according to the design parameters, and input the hoisting system simulation parameters including drum structure parameters, hoisting wire rope parameters, system control parameters, hoisting height, and hoisting load; b-2 Under static equilibrium conditions, solve the wire rope tension characteristics, load velocity characteristics, load displacement characteristics, and wire rope vibration characteristic parameters; b-3 Bring the parameters solved by AME into the Abaqus finite element simulation model to obtain the fatigue damage characteristics of the wire rope at the drum and the sheave; b-4 Change different input parameters and repeat b1-b3 operations to obtain simulation results under different working conditions and parameters of the multi-layer winding entity of the lifting wire rope.

5. The reliability life assessment method for ultra-deep shaft hoisting wire rope according to claim 3 is characterized by: In step e, the sensor data is first subjected to wavelet transformation and compressed sensing noise reduction processing, and then the same working condition supplementary data, different working condition prediction data, and monitoring information noise reduction data are subjected to feature extraction and fusion processing and uploaded to the cloud network. The detailed steps e are shown in e-1 to e-4: e-1 performs detrending, high-pass and low-pass time-domain noise reduction processing on the speed, displacement, mechanics and other signals collected by the sensor, and then performs Fourier transform on the signal to observe its phase and frequency domain characteristics, and performs filtering, dilation, corrosion and wavelet transform noise reduction processing on the collected infrared images and high-definition images; e-2 normalizes, interpolates, segments, increases dimension and visualizes the single-dimensional signal after noise reduction processing; e-3 performs image processing such as normalization and grayscale conversion on the dimension-enhanced visualization signal and the image noise reduction signal; e-4 performs image fusion, feature fusion and decision fusion on the processed signals, simulation prediction signals and simulation supplementary signals in the previous step, and then transmits the feature extraction data and fusion data to the cloud server to form a real-time updated multi-source information intelligent database.

6. The reliability life assessment method for ultra-deep shaft hoisting wire rope according to claim 3 is characterized in that: The steps in step f to clarify the prerequisites for the occurrence of adverse winding conditions in the lifting system are: f-1Calculate the minimum cut set T of the fault tree T=A1A2A3 =(A4+X1)(A5A6)(X2X3X4X5) =(X1+X6+X7+X8)(X9X 10 X 11 )(X 12 X 13 X 14 )(X2X3X4X5) =X1X2X3X4X5X9X 10 X 11 X 12 X 13 X 14 +X6X2X3X4X5X9X 10 X 11 X 12 X 13 X 14 +X7X2X3X4X5X9X 10 X 11 X 12 X 13 X 14 +X8X2X3X4X5X9X 10 X 11 X 12 X 13 X 14 K1={X1,X2,X3,X4,X5,X9,X 10 ,X 11 ,X 12 ,X 13 ,X 14 } K2={X6,X2,X3,X4,X5,X9,X 10 ,X 11 ,X 12 ,X 13 ,X 14 } K3={X7,X2,X3,X4,X5,X9,X 10 ,X 11 ,X 12 ,X 13 ,X 14 } K4={X8,X2,X3,X4,X5,X9,X 10 ,X 11 ,X 12 ,X 13 ,X 14 }; f-2 Calculation of structural importance of unfavorable winding conditions of hoisting wire rope (X2=X3=X4=X5=X9=X 10 =X 11 =X 12 =X 13 =X 14 )>(X1=X6=X7=X8) That is, the order of importance of events is: (large deviation of rope groove pitch = failure of transition guide device = drum stall = abnormal winding method = small preload of wire rope = wear and breakage of wire = large impact load = large rope deflection angle = poor lubrication condition) > (electromagnetic interference = slow transmission rate = high temperature and humidity = immature signal characterization method).

7. The reliability life assessment method for ultra-deep shaft hoisting wire rope according to claim 3 is characterized in that: The steps of the disjointness reduction algorithm (SDP) in step h are: Disjoint processing of h-1 minimum cut sets: K1=X1X2X3X4X5X9X 10 X 11 X 12 X 13 X 14 K2=X6X2X3X4X5X9X 10 X 11 X 12 X 13 X 14 K3=X7X2X3X4X5X9X 10 X 11 X 12 X 13 X 14 K4=X8X2X3X4X5X9X 10 X 11 X 12 X 13 X 14 ; Simplified calculation of h-2: Let X2X3X4X5X9X 10 X 11 X 12 X 13 X 14 =X t ,but K1=X1X t ;K2=X6X t ;K3=X7X t ;

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