A bracket fault judgment method based on electro-hydraulic control system

Abstract

The present invention discloses a method for judging various common faults of a bracket based on an electro-hydraulic control system, wherein the electro-hydraulic control system includes a top beam inclination sensor, a mounting plate, a front connecting rod inclination sensor, a rear column pressure sensor, a base inclination sensor, a front column pressure sensor, a front slip stroke sensor, and a database server, and signal output ends of the top beam inclination sensor, the mounting plate, the front connecting rod inclination sensor, the rear column pressure sensor, the base inclination sensor, the front column pressure sensor, and the front slip stroke sensor are all connected to the signal input end of the database server; the method for judging various common faults of the bracket includes judging a safety valve failure, judging a safety valve setting value change, judging a column leakage failure, judging a bracket sensor distortion failure, judging a bracket load height abnormality failure, and judging a pipeline blockage failure.

Description

Technical Field

[0001] The present invention relates to the field of coal mines, and in particular to a support fault judgment method based on an electro-hydraulic control system. Background Art

[0002] During the load-bearing process of the hydraulic support, the hydraulic valve often malfunctions. Currently, the hydraulic support faults are only checked when the hydraulic support cannot perform the supporting work or the supporting work of the hydraulic support will affect the coal mine operation, and the troubleshooting method is mostly manual. Due to factors such as the large number of hydraulic supports, the difficulty of troubleshooting, and the wide variety of faults, there are problems such as low troubleshooting efficiency and low troubleshooting accuracy when troubleshooting the hydraulic support faults, which brings certain inconveniences to the use of hydraulic supports in coal mines. Summary of the Invention

[0003] The purpose of the present invention is to address the above problems and provide a method for quickly determining various common faults of a bracket based on an electro-hydraulic control system.

[0004] In order to achieve the above object, the technical solution of the present invention is:

[0005] A method for judging a support fault based on an electro-hydraulic control system, wherein the electro-hydraulic control system includes a top beam inclination sensor, a mounting plate, a front connecting rod inclination sensor, a rear column pressure sensor, a base inclination sensor, a front column pressure sensor, a front slip stroke sensor, and a database server. The top beam inclination sensor is mounted on the top beam of the hydraulic support, the mounting plate is mounted on the front connecting rod of the hydraulic support, the front connecting rod inclination sensor is mounted on the mounting plate, the rear column pressure sensor is mounted on the rear column of the hydraulic support, the base inclination sensor is mounted on the base of the hydraulic support, and the front column pressure sensor is mounted on the front column of the hydraulic support. The front slip stroke sensor is installed between the base of the hydraulic support and the ground; the signal output ends of the top beam inclination sensor, the mounting plate, the front connecting rod inclination sensor, the rear pillar pressure sensor, the base inclination sensor, the front pillar pressure sensor, and the front slip stroke sensor are all connected to the signal input end of the database server; the common fault judgments of the support include the judgment of safety valve failure, the judgment of safety valve setting value change, the judgment of column leakage, the judgment of support sensor distortion, the judgment of support load height abnormality, and the judgment of pipeline blockage.

[0006] Furthermore, the determination of safety valve failure includes the following steps:

[0007] S11. Obtain the safety valve opening pressure P set by the manufacturer 额定 , real-time collection of pressure information of the front column pressure sensor and the rear column pressure sensor, the current column pressure or rear column pressure P nSatisfy P n ≥P 额定 When the pressure information database of the corresponding column is used, the pressure data within 5 minutes are obtained, which are P1, P2, P3, P4, P5...P n ;

[0008] S12, if all pressure data within 5 minutes meet P x ≥P 额定 When x=1,2,3,…n, it is determined that the hydraulic support has a safety valve failure.

[0009] Furthermore, the determination of a change in the safety valve setting value includes the following steps:

[0010] S21. Obtain the safety valve opening pressure P set by the manufacturer. 额定 , real-time collection of pressure information of the front column pressure sensor and the rear column pressure sensor, the current column pressure or rear column pressure P n Satisfy P n ≥0.8P 额定 When the pressure information database of the corresponding column is obtained in reverse order, P m ≥0.8P 额定 When the pressure data is less than 0.8P 额定 When , stop data collection, where n=1,2,3,…m;

[0011] S22. Sort the pressure data by generation time, calculate the peak points and valley points generated by all the pressure data, and determine whether the sum of the number of peak points and valley points is greater than 3; if greater than 3, proceed to step 23; if not greater than 3, wait for the next calculation;

[0012] S23. Calculate the mean of all peak points and the mean of all valley points respectively, calculate the average of the mean of all peak points and the mean of all valley points again, and use the average as the safety valve setting value.

[0013] Furthermore, the judgment of the column leakage fault includes the following steps:

[0014] S31, obtaining the set threshold pressure P 阈值 , real-time collection of pressure information of the front column pressure sensor and the rear column pressure sensor, the current column pressure or rear column pressure P n Satisfy P n ≥P 阈值 When the pressure information database of the corresponding column is used, the pressure data within 10 minutes are obtained, which are P1, P2, P3, P4, P5...P n ;

[0015] S32, if the pressure data within 10 minutes satisfies P1≥P2≥P3≥P4≥P5…≥P n , it is judged that the hydraulic support has a column leakage fault.

[0016] Furthermore, when judging the distortion failure of the bracket sensor,

[0017] S41, collecting pressure information of the front pillar pressure sensor and the rear pillar pressure sensor in real time;

[0018] S42. If three consecutive monitoring pressure data of the front column pressure or the rear column pressure are all 0 or 60, it is determined that the hydraulic support has a support sensor distortion fault.

[0019] Furthermore, the determination of the abnormal support height failure includes the following steps:

[0020] S51, collecting monitoring data of the top beam inclination sensor, the front link inclination sensor, the base inclination sensor, and component size data of the hydraulic support;

[0021] S52, record the monitoring data of the top beam inclination sensor as α, the monitoring data of the front link inclination sensor as β, the monitoring data of the base inclination sensor as γ, and the inclination angle of the mounting plate as a fixed value δ;

[0022] S53, calculating and obtaining the mining height h of the hydraulic support;

[0023] S54. Perform comparative analysis based on the mining height h of each hydraulic support; when the mining height h of the hydraulic support is not within the set range, it is determined that the hydraulic support has an abnormal support bearing height failure; calculate the real-time support heights of all hydraulic supports on the working surface, and solve the average of the real-time heights of all hydraulic supports, and compare the real-time heights of each hydraulic support on the working surface with the average of the real-time heights of all hydraulic supports. If there is a real-time height higher or lower than 85% of the average of all hydraulic supports, it is determined that the hydraulic support has an abnormal support bearing height failure.

[0024] Furthermore, the determination of a pipeline obstruction fault includes the following steps:

[0025] S61, collecting data information generated when the hydraulic support is in motion;

[0026] S62. Using a big data scatter point fitting method, fit the functional relationship between the column lowering and column raising action time and the pressure change of the front column and the rear column. Simultaneously, fit the functional relationship between the rack moving and slide pushing action time and the front slide stroke change to obtain a scatter point fitting image.

[0027] S63, determining the effective range radius of the function fitting curve based on the scatter point fitting image, and determining that the data not within the effective range of the function fitting curve is a pipeline blockage fault;

[0028] S64. Quantitatively process the column lowering and raising action times, the pressure changes of the front and rear columns, the frame moving and sliding action times, and the front sliding stroke change as the input layer of the BP neural network model.

[0029] S65, using the pipeline blockage fault as the output layer of the BP neural network model;

[0030] S66, using step S63 to determine the model parameters of the BP neural network model, to obtain an initial BP neural network model;

[0031] S67. Divide the historical data information into a training set and a test set, use the training set to train the initial BP neural network model, use the test set to test the trained initial BP neural network model and correct the model parameters, and finally obtain the BP neural network model; the pipeline blockage fault can be judged by the BP neural network model.

[0032] Compared with the prior art, the present invention has the following advantages and positive effects:

[0033] The present invention is based on the actual on-site load-bearing conditions of the hydraulic support. Professional and technical personnel combine the on-site action analysis of various hydraulic supports with the self-learning of equipment big data fault diagnosis to build various common fault models of hydraulic supports. By setting the equipment's independent key parameter extraction and analysis program and comparing it with various common fault models of the support, it can effectively replace manual hydraulic support fault troubleshooting operations. After discovering faulty equipment and abnormal phenomena, it can automatically perform fault warning and fault diagnosis, realize fault diagnosis, fault warning and provide solution suggestions; it ensures the safe and effective operation of the working face equipment while reducing the workers' work intensity, improving the troubleshooting efficiency of the hydraulic support, and effectively improving the fault troubleshooting accuracy of the hydraulic support, which brings convenience to the use and operation of the hydraulic support. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0035] Figure 1 It is a structural diagram of the electro-hydraulic control system;

[0036] Figure 2 This is a schematic diagram for calculating the support bearing height;

[0037] Figure 3This is a schematic diagram of scatter point fitting. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts, any modifications, equivalent replacements, improvements, etc., shall be included in the scope of protection of the present invention.

[0039] The present invention discloses a bracket fault diagnosis method based on an electro-hydraulic control system. The common bracket fault diagnosis methods include the diagnosis of "safety valve failure", "safety valve setting value change", "column leakage", "bracket sensor distortion", "bracket bearing height abnormality", and "pipeline blockage".

[0040] like Figure 1 As shown, the electro-hydraulic control system includes a top beam inclination sensor 1, fixing bolts 2, a mounting plate 3, a front connecting rod inclination sensor 4, a rear column pressure sensor 5, a base inclination sensor 6, a front column pressure sensor 7, a front sliding stroke sensor 8, an electronic control operation platform 9, a database server 10, an application server 11, a chute display 12, a switch 13, a workstation 14, a printer 15, and a large-screen display 16;

[0041] The top beam inclination sensor 1 is installed on the top beam of the hydraulic support. The mounting plate 3 is installed on the front link of the hydraulic support through the fixing bolts 2. The front link inclination sensor 4 is installed on the mounting plate 3. The rear column pressure sensor 5 is installed on the rear column of the hydraulic support. The base inclination sensor 6 is installed on the base of the hydraulic support. The front column pressure sensor 7 is installed on the front column of the hydraulic support. The front slip travel sensor 8 is installed between the base of the hydraulic support and the ground.

[0042] The electro-hydraulic control system can collect data such as the inclination angle of the underground support top beam, the inclination angle of the front connecting rod, the inclination angle of the base, the pressure of the front column of the support, the pressure of the rear column of the support, the front sliding stroke of the support, and the support action signal in real time, and can use underground optical fiber and industrial ring network to transmit the data to the ground.

[0043] The safety valve is used to protect the cylinder pressure safety of the hydraulic support. When the set safety pressure is reached, it automatically releases the pressure to ensure that the medium pressure in the support cylinder remains below the preset pressure. This can ensure the normal operation of the support and prevent the occurrence of cylinder explosion accidents.

[0044] The steps for judging "safety valve failure" are as follows:

[0045] S31, real-time warning of safety valve failure of the bracket, obtain the manufacturer's production setting of the safety valve opening pressure P 额定 , real-time collection of support pressure information, the new support front column pressure, support rear column pressure P n Satisfy P n ≥P 额定 When the pressure data in the last 5 minutes are obtained from the corresponding column database, they are P1, P2, P3, P4, P5...P n ;

[0046] S32. If the pressure data in the last 5 minutes all meet P x ≥P 额定 When x=1,2,3,…n;

[0047] S33. An early warning of "safety valve failure" should be given on the underground drift display. The warning description is: the safety valve fails and does not open when the set warning force is reached.

[0048] The steps for changing the safety valve setting value are as follows:

[0049] S41. Obtain the manufacturer's production setting safety valve opening pressure P 额定 , real-time collection of support pressure information, the new support front column pressure, support rear column pressure P n Satisfy P n ≥0.8P 额定 When the pressure P is obtained in the corresponding column database in reverse order, m Satisfy P m ≥0.8P 额定 Pressure data, when encountering dissatisfaction, stop data collection, where n=1,2,3,…m;

[0050] S42, sorting the pressures according to generation time, calculating the peak points and valley points generated by all pressure data, and determining whether the sum of the calculated peak points and valley points is greater than 3;

[0051] If it is greater than 3, go to step S43;

[0052] If it is not greater than 3, discard it and wait for the next calculation;

[0053] S43. Calculate the mean of all peak points and the mean of all valley points respectively, calculate the mean of all peak points and the mean of all valley points again, and define it as the "safety valve program judgment value".

[0054] The steps to judge the "column leakage fault" are as follows:

[0055] S51, obtaining the set threshold pressure P 阈值, real-time collection of support pressure information, the new support front column pressure, support rear column pressure P n Satisfy P n ≥P 阈值 When the pressure data in the last 10 minutes are obtained from the corresponding column database, they are P1, P2, P3, P4, P5...P n ;

[0056] S52, if P1≥P2≥P3≥P4≥P5…≥P n The negative growth of the support pressure is inconsistent with the mine pressure theory, so this phenomenon is judged to be a "column leakage failure."

[0057] The steps for troubleshooting the "bracket sensor distortion" fault are as follows:

[0058] S61, bracket sensor distortion refers to the bracket sensor display data abnormality, showing "0 (minimum range)" value, "60 (full range)" value for a long time, the sensor can no longer feedback the real scene information;

[0059] S62. Collect the support pressure information in real time. If three consecutive monitoring data of the support front column pressure or the support rear column pressure are "0 (minimum range)" or "60 (full range)", an early warning of "support sensor distortion" fault will be issued.

[0060] The steps for diagnosing the "bracket bearing height abnormality" fault are as follows:

[0061] S71. Calculate the real-time load-bearing height of each bracket based on the monitoring data of the top beam inclination sensor, the front link inclination sensor, and the base inclination sensor and the geometric dimensions of each structural component of the bracket;

[0062] S72, such as Figure 2 As shown, let the top beam tilt sensor monitoring data be recorded as α, the front link tilt sensor monitoring data be recorded as β, and the base tilt sensor monitoring data be recorded as γ, where α, β, and γ all satisfy the requirement that upward tilt is positive and downward tilt is negative. The mounting plate angle is a fixed value δ;

[0063] S73, then the inclination angle of AC is ε = 180-δ-β;

[0064] S74. According to the cosine theorem, BC=(AB 2 +AC 2 -cos(ε+γ+∠EAB)*2AB*AC) 1 / 2 In △BDC, ∠CBD=arccos((CB 2 +BD 2 -CD 2 ) / (2BC*BD)), ∠ABC= arccos((AB 2 +BC2 -AC 2 ) / (2AB*BC));

[0065] S75. Define point B' as the coordinate origin (0,0), with the positive x direction pointing to the left horizontally and the positive y direction pointing upward vertically.

[0066] S76. Find the coordinates of points A, B, C, and D:

[0067] A(AB'*cos(∠AB'K'), AB'*sin(∠AB'K'));

[0068] B(0,BB');

[0069] C(AB'*cos(∠AB'K')-AC*cos(ε+γ), AB'*sin(∠AB'K')+AC*sin(β+γ));

[0070] D(0 -BD*cos(180 º-∠EAB-∠ABC-∠CBD), BB' + BD*sin(180 º-∠EAB-∠ABC-∠CBD));

[0071] S77, find ∠DCJ;

[0072] ∠DCJ=arctan((AB'*sin(∠AB'K')+AC*sin(ε+γ)-BB'-BD*sin(180º-∠EAB-∠ABC-∠CBD)) / ((AB'*cos(∠AB'K')-AC*cos(ε+γ)+0 +BD*cos(180 º-∠EAB-∠ABC-∠CBD)));

[0073] S78. Let DI ⊥ CG. In △CID, IC = |DH - CG|, so ∠ICD = arccos (IC / CD)

[0074] S79, the inclination angle of the shield beam is θ = ∠ICD - ∠DCJ;

[0075] S710. Mining height h=A'B'*sin(γ)+AA'+AC*sin(ε+γ) +CG*sin(90-θ)+GU*sin(θ)+VU*sin(α+∠U'UV);

[0076] S711. Compare and analyze the mining height of each support. There are two situations. Situation 1: When the mining height is not within the set range, an alarm is issued for "abnormal support bearing height". Situation 2: Calculate the real-time support height of all supports on the working surface, and solve the average of the real-time heights of all supports. Compare the real-time height of each support on the working surface with the average of the real-time heights of all supports. If there is a real-time height that is higher or lower than 85% of the average of the real-time heights of all supports, an alarm should be issued for the "abnormal support bearing height" fault of the support.

[0077] The steps for judging the "pipeline obstruction" fault are as follows:

[0078] S81, real-time collection of control signals generated by the support action (including column lowering, frame moving, column raising, and sliding), support front and rear column pressure, front sliding stroke and other data;

[0079] S82. Use the BP neural network model and learning algorithm to perform self-learning for fault diagnosis. Based on the roof and floor inclination angles and the lithology of the roof and floor as basic features, use the big data scatter point fitting method to fit the functional relationship between the column lowering and column raising action time and the change in the pressure of the front and rear columns, and the functional relationship between the frame moving and slide pushing action time and the change in the front slide stroke, and obtain the scatter point fitting image, such as Figure 3 As shown;

[0080] S83. Determine the effective range radius of the function fitting curve based on the scatter point fitting image to ensure that most data can effectively fall within the range. Based on the fitting function and the effective radius, define data that is not within the effective range as a "pipeline blockage" fault.

[0081] S84. Quantitatively process the column lowering and raising action times, the pressure changes of the front and rear columns, the frame moving and sliding action times, and the front sliding stroke changes as the input layer of the BP neural network model.

[0082] S85, using the "pipeline blockage" fault as the output layer of the BP neural network model;

[0083] S86, using step S83 to predetermine the model parameters of the BP neural network model to obtain an initial BP neural network model;

[0084] S87. Divide the historical data into a training set and a test set, use the training set to train the initial BP neural network model, use the test set to test the trained initial BP neural network model, and correct the model parameters to obtain the BP neural network model. The BP neural network model can be used to judge the pipeline blockage fault.

[0085] The present invention is based on the actual on-site load-bearing conditions of the hydraulic support. Professional and technical personnel combine the on-site action analysis of various hydraulic supports with the self-learning of equipment big data fault diagnosis to build various common fault models of hydraulic supports. By setting the equipment's independent key parameter extraction and analysis program and comparing it with various common fault models of the support, it can effectively replace manual hydraulic support fault troubleshooting operations. After discovering faulty equipment and abnormal phenomena, it can automatically perform fault warning and fault diagnosis, realize fault diagnosis, fault warning and provide solution suggestions; it ensures the safe and effective operation of the working face equipment while reducing the workers' work intensity, improving the troubleshooting efficiency of the hydraulic support, and effectively improving the fault troubleshooting accuracy of the hydraulic support, which brings convenience to the use and operation of the hydraulic support.

Claims

1. A method for determining a bracket fault based on an electro-hydraulic control system, characterized in that: The electro-hydraulic control system includes a roof beam inclination sensor, a mounting plate, a front connecting rod inclination sensor, a rear pillar pressure sensor, a base inclination sensor, a front pillar pressure sensor, a front slip travel sensor, and a database server. The signal output terminals of the roof beam inclination sensor, mounting plate, front connecting rod inclination sensor, rear pillar pressure sensor, base inclination sensor, front pillar pressure sensor, and front slip travel sensor are all connected to the signal input terminal of the database server. The bracket fault judgment includes judgment on safety valve failure, judgment on safety valve setting value change, judgment on pillar leakage, judgment on bracket sensor distortion, judgment on bracket load height abnormality, and judgment on pipeline blockage; The judgment of safety valve failure includes the following steps: S11. Obtain the safety valve opening pressure P set by the manufacturer 额定 , real-time collection of pressure information of the front column pressure sensor and the rear column pressure sensor, the current column pressure or rear column pressure P n Satisfy P n ≥P 额定 When the pressure information database of the corresponding column is used, the pressure data within 5 minutes are obtained, which are P1, P2, P3, P4, P5...P n ; S12, if all pressure data within 5 minutes meet P x ≥P 额定 When x=1,2,3,…n, it is judged that the hydraulic support has a safety valve failure; The judgment of the change of the safety valve setting value includes the following steps: S21. Obtain the safety valve opening pressure P set by the manufacturer. 额定 , real-time collection of pressure information of the front column pressure sensor and the rear column pressure sensor, the current column pressure or rear column pressure P n Satisfy P n ≥0.8P 额定 When the pressure information database of the corresponding column is obtained in reverse order, P m ≥0.8P 额定 When the pressure data is less than 0.8P 额定 When , stop data collection, where n=1,2,3,…m; S22. Sort the pressure data by generation time, calculate the peak points and valley points generated by all the pressure data, and determine whether the sum of the number of peak points and valley points is greater than 3; if greater than 3, proceed to step 23; if not greater than 3, wait for the next calculation; S23, respectively calculating the mean of all peak points and the mean of all valley points, calculating the average of the mean of all peak points and the mean of all valley points again, and using the average as the safety valve setting value; The judgment of column leakage failure includes the following steps: S31, obtaining the set threshold pressure P 阈值 , real-time collection of pressure information of the front column pressure sensor and the rear column pressure sensor, the current column pressure or rear column pressure P n Satisfy P n ≥P 阈值 When the pressure information database of the corresponding column is used, the pressure data within 10 minutes are obtained, which are P1, P2, P3, P4, P5...P n ; S32, if the pressure data within 10 minutes satisfies P1≥P2≥P3≥P4≥P5…≥P n , it is judged that the hydraulic support has a column leakage fault; When judging the distortion fault of the bracket sensor, S41, collecting pressure information of the front pillar pressure sensor and the rear pillar pressure sensor in real time; S42: If three consecutive monitoring pressure data of the front column pressure or the rear column pressure are all 0 or 60, it is determined that the hydraulic support has a support sensor distortion fault; The judgment of abnormal support height failure includes the following steps: S51, collecting monitoring data of the top beam inclination sensor, the front link inclination sensor, the base inclination sensor, and component size data of the hydraulic support; S52, record the monitoring data of the top beam inclination sensor as α, the monitoring data of the front link inclination sensor as β, the monitoring data of the base inclination sensor as γ, and the inclination angle of the mounting plate as a fixed value δ; S53, calculating and obtaining the mining height h of the hydraulic support; S54. Perform comparative analysis based on the mining height h of each hydraulic support; when the mining height h of the hydraulic support is not within the set range, it is determined that the hydraulic support has an abnormal support bearing height failure; calculate the real-time support heights of all hydraulic supports on the working surface, and solve the average of the real-time heights of all hydraulic supports, and compare the real-time heights of each hydraulic support on the working surface with the average of the real-time heights of all hydraulic supports. If there is a real-time height higher or lower than 85% of the average of the real-time heights of all hydraulic supports, it is determined that the hydraulic support has an abnormal support bearing height failure.

2. The method for determining a bracket fault based on an electro-hydraulic control system according to claim 1, wherein: The judgment of pipeline blockage fault includes the following steps: S61, collecting data information generated when the hydraulic support is in motion; S62. Using a big data scatter point fitting method, fit the functional relationship between the column lowering and column raising action time and the pressure change of the front column and the rear column. Simultaneously, fit the functional relationship between the rack moving and slide pushing action time and the front slide stroke change to obtain a scatter point fitting image. S63, determining the effective range radius of the function fitting curve based on the scatter point fitting image, and determining that the data not within the effective range of the function fitting curve is a pipeline blockage fault; S64. Quantitatively process the column lowering and raising action times, the pressure changes of the front and rear columns, the frame moving and sliding action times, and the front sliding stroke change as the input layer of the BP neural network model. S65, using the pipeline blockage fault as the output layer of the BP neural network model; S66, using step S63 to determine the model parameters of the BP neural network model to obtain an initial BP neural network model; S67. Divide the historical data information into a training set and a test set, use the training set to train the initial BP neural network model, use the test set to test the trained initial BP neural network model and correct the model parameters, and finally obtain the BP neural network model; the pipeline blockage fault can be judged by the BP neural network model.

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