Conveying belt surface defect identifying and positioning method based on sweeper vibration signals

Abstract

The invention discloses a conveyor belt surface defect recognition and positioning method based on a sweeper vibration signal, and belongs to the field of belt surface defect recognition and positioning, and the method comprises the following steps: S1, collecting a vibration signal generated when a sweeper scraper collides with a defect point by using a vibration sensor, and collecting a speed signal of a belt conveyor by using a speed sensor; s2, preprocessing the speed signal by using a speed algorithm, and judging the running state of the belt conveyor by using a processing result; s3, the operation state, the vibration signal and the speed signal of the belt conveyor are combined, and defect points are recognized; s4, calculating the relative distance between the defect point identified in the step S3 and the scraper by using a distance algorithm; and S5, outputting and displaying a calculation result. The conveyor belt surface defect identification and positioning method based on the sweeper vibration signal is not influenced by the operating environment and definition, and has the advantages of high reliability, real-time monitoring, high adaptability and the like.

Description

Technical Field

[0001] The invention relates to the technical field of belt surface defect recognition and positioning, and in particular to a conveyor belt surface defect recognition and positioning method based on a sweeper vibration signal. Background Art

[0002] Belt conveyors are key equipment in the coal transportation system of coal-fired power plants, responsible for transporting coal from one place to another. However, in actual operation, due to seasonal factors such as wet coal in the rainy season and low coal temperature in winter, coal is easily adhered to the surface of the conveyor belt. This adhesion phenomenon can cause adverse conditions such as conveyor belt jumping and deviation, thus causing safety hazards. Especially in winter, after the frozen coal layer is bent by the surface increase roller and vibrated by the return roller, part of the adhesive material will fall off, resulting in serious coal scattering along the return line of the belt conveyor, seriously affecting the normal operation of the conveyor.

[0003] In order to remove the sticky coal on the belt surface in time, the prior art applies belt cleaners to the conveyors of coal-fired power plants. The positive pressure applied between the scraper of the belt cleaner and the conveyor belt forms contact and friction, thereby removing the coal slime adhering to the conveyor belt surface. However, the existing belt cleaning device usually directly contacts the belt surface through the cleaning scraper. During the operation of the belt, the purpose of removing the adhesive on the belt surface is achieved through the relative movement and friction between the scraper and the belt surface. At this time, when there are defects on the belt surface or protrusions formed after repair, the scraper of the cleaner will repeatedly roll over the defects or protrusions, causing further damage to the belt.

[0004] To solve this problem, existing research has further proposed a monitoring method based on visual technology to monitor belt surface defects in real time. However, due to the harsh working environment of coal conveyor belts, factors such as dust and light changes will affect the clarity of the image and the monitoring effect, making the above method still face challenges in practical application. Summary of the invention

[0005] The purpose of the present invention is to provide a method for identifying and locating conveyor belt surface defects based on a sweeper vibration signal to solve the above-mentioned technical problems.

[0006] To achieve the above object, the present invention provides a method for identifying and locating conveyor belt surface defects based on a cleaner vibration signal, comprising the following steps:

[0007] S1. Use a vibration sensor to collect the vibration signal generated when the scraper of the sweeper collides with the defect point, and use a speed sensor to collect the speed signal of the belt conveyor;

[0008] S2. Preprocess the speed signal using a speed algorithm, and use the processing result to determine the operating status of the belt conveyor;

[0009] S3, identifying the defective point by combining the running state of the belt conveyor determined in step S2 and the vibration signal and speed signal collected in step S1;

[0010] S4, using a distance algorithm to calculate the relative distance between the defect point identified in step S3 and the scraper;

[0011] S5. Output and display the calculation result of step S4.

[0012] Preferably, in step S1, a data acquisition card is used to synchronously acquire the vibration signal and the speed signal, and the data acquisition card is set to package the data every 2 seconds and send it to the processing terminal.

[0013] Preferably, step S2 specifically includes the following steps:

[0014] S21. The speed sensor uses a roller speed sensor. The roller of the roller speed sensor is fitted to the belt surface of the belt conveyor. The roller rotates through the friction between the belt and the belt. The roller rotates one circle to generate a pulse signal. The speed is obtained through two adjacent rising edges to calculate the speed V of the belt conveyor. 皮带速度 :

[0015]

[0016] In the formula, C 滚轮周长 Indicates the roller circumference of the roller speed sensor; P 索引值差值 It indicates the difference between the two horizontal coordinates corresponding to the same vertical coordinate position of two adjacent pulse signals generated by rolling two circles in sequence; f s Indicates the sampling frequency;

[0017] S22. Determine the operating status of the belt conveyor based on the two sets of speed values ​​calculated in step S21.

[0018] Preferably, in step S21, the roller diameter R is set to 0.15 m, then C 滚轮周长 =πR≈0.47m.

[0019] Preferably, in step S22, two adjacent groups of speed data are set to V x and V x+1 , and set the judgment logic as follows:

[0020] When V x = 0 and V x+1 =0, it is determined that the belt conveyor is in a stopped state;

[0021] When V x = 0 and V x+1 When >0, it is determined that the belt conveyor is in the start state;

[0022] When V x >0 and V x+1 When >0, it is determined that the belt conveyor is in acceleration, constant speed or deceleration state;

[0023] When V x >0 and V x+1 =0, it is determined that the belt conveyor is in the shutdown state.

[0024] Preferably, step S3 specifically includes the following steps:

[0025] S31. Based on the running status of the belt conveyor, select the following processing logic:

[0026] When the belt conveyor is in a stopped state, the vibration sensor, speed sensor, data acquisition card and processing terminal all enter a dormant state;

[0027] When the belt conveyor is in the on state, the processing terminal clears the data and waits for the distance calculation to be retriggered;

[0028] When the belt conveyor is in an acceleration, constant speed or deceleration state, it is determined whether there is a vibration signal. If so, the distance calculation is triggered and step S32 is executed. Otherwise, no action is taken.

[0029] When the belt conveyor is in shutdown state, the processing terminal stops calculating and displays the distance calculation result;

[0030] S32, using VMD algorithm to reduce noise on vibration signal and speed signal;

[0031] S321. Assuming that the original signal is s(t), the optimization problem of the VMD method is expressed as:

[0032]

[0033] In the formula, u k (t) represents the kth intrinsic mode function, ω k represents the frequency of the kth intrinsic mode function, k represents the number of intrinsic mode functions, and λ is the regularization parameter;

[0034] S322, initialization frequency ω k and the intrinsic mode function u k (t);

[0035] S323, iteratively update each intrinsic mode function u through alternating optimization k (t) and frequency ω k , until the stopping condition is met;

[0036] S324, reconstructing the signal using the decomposed intrinsic mode function and the corresponding frequency to achieve signal noise reduction;

[0037] S33, determine whether there is a defect point: take the kurtosis value of the belt conveyor during normal operation as the first threshold, calculate the kurtosis value of the vibration data after VMD denoising, and then calculate the deviation between the kurtosis value of the vibration data after denoising and the first threshold. If the deviation is greater than the set value, it is determined that the defect point exists, and step S34 is executed, otherwise no action is taken;

[0038] S34, peak extraction:

[0039]

[0040] N z =t z f s (5);

[0041] Where, t is the sampling time of a set of vibration data after noise elimination; N is the number of samples of vibration data after noise elimination; t z is the critical sampling time; N z is the critical error sampling number; V 额 is the rated working speed of the belt conveyor; Z is the maximum allowable error in meters;

[0042] S35. Calculate the critical error sampling number N z The ratio k to the number of samples N of the vibration data after noise elimination z :

[0043]

[0044] S36. Take k peak points from each set of vibration data after noise elimination, and the calculation formula of the peak point k is as follows:

[0045] k=[k z ]+1 (7);

[0046] S37. Consider the extracted peak point as a defect point.

[0047] Preferably, in step S33, the kurtosis value calculation formula of the vibration data after noise elimination is as follows:

[0048]

[0049] Where, X i represents the corresponding data point, represents the mean value of the vibration data after noise elimination, and N represents the total number of data points.

[0050] Preferably, in step S34, when the peak value is extracted, the horizontal and vertical coordinate values ​​corresponding to the peak value are saved simultaneously, and it is set that when the interval between two peak points is less than a critical error sampling number interval, the previous peak point is extracted for calculation.

[0051] Preferably, step S4 specifically includes the following steps:

[0052] S41, set the scraper as the calculation starting point, and sum the distance traveled by the defect point within the sampling time of each set of noise-removed vibration data:

[0053]

[0054] Where, distance 总 is the relative distance between the defect point and the scraper; N 1 is the index value when the peak point appears; NN 1 Indicates the number of samples that have been sampled within the critical error sampling interval when the peak value appears; V 1 V is the speed value collected in the critical error sampling interval when the peak point is collected; 2 V is the speed value collected in a critical error sampling interval after the peak point is collected; k is the speed value collected in the kth critical error sampling interval after the peak point is collected;

[0055] S42. Set the total length of one circle of the belt conveyor as the second threshold value. If the calculated distance traveled by the defective point is not less than the second threshold value, the distance calculation of this defective point is eliminated. Otherwise, the belt conveyor enters the next circle. In the next circle, if the defective point is scraped off by the scraper, no action is taken. Otherwise, recalculation of the distance is triggered.

[0056] Therefore, the present invention adopts the above-mentioned conveyor belt surface defect identification and positioning method based on the sweeper vibration signal, which has the following beneficial effects:

[0057] 1. Not affected by the environment: The method based on vibration signals is not affected by environmental factors and can work stably under various conditions;

[0058] 2. High reliability: The vibration signal is very sensitive to changes in the mechanical structure and can reliably detect subtle defects on the belt surface even in low-light or dusty environments;

[0059] 3. Real-time monitoring: Vibration signals can be collected and analyzed in real time to detect and locate belt surface defects immediately, which helps to take timely measures to prevent defects from further expanding;

[0060] 4. Strong adaptability: It is not only suitable for newly installed equipment, but also for belts that have been used for a period of time and may be worn or repaired. It can effectively cope with defect detection needs in various situations;

[0061] 5. Easy to integrate: The vibration sensor is small in size and easy to install. It can be easily integrated into the existing conveying system without large-scale modification of the equipment.

[0062] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a flow chart of the conveyor belt surface defect identification and positioning method based on the sweeper vibration signal of the present invention;

[0064] Figure 2 This is the result diagram of the simulation experiment 0s-2s vibration signal processing;

[0065] Figure 3 This is the result diagram of the simulation experiment 2s-4s vibration signal processing;

[0066] Figure 4 This is the result diagram of the 4s-6s vibration signal processing in the simulation experiment;

[0067] Figure 5 This is the result diagram of the 6s-8s vibration signal processing in the simulation experiment;

[0068] Figure 6 This is the result diagram of vibration signal processing for 8s-10s in simulation experiment;

[0069] Figure 7 This is the result diagram of the vibration signal processing of the simulation experiment 10s-12s;

[0070] Figure 8 This is the result diagram of vibration signal processing in simulation experiment 12s-14s;

[0071] Fig. 9 This is the result diagram of vibration signal processing for 14s-16s of simulation experiment;

[0072] Fig.10 This is the result diagram of vibration signal processing during the simulation experiment 16s-18s;

[0073] Fig.11 This is the result of vibration signal processing during the simulation experiment 18s-20s. DETAILED DESCRIPTION

[0074] In the description of the present invention, it should be noted that the terms "upper", "lower", "inside", "outside", etc. indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, or the positions or positional relationships in which the invented product is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of the present invention, it should also be noted that, unless otherwise clearly specified and limited, the terms "setting", "installation", and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0075] The embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0076] According to the mechanism that belt surface defects excite the scraper to produce impact vibration, the vibration kurtosis characteristics of the scraper under belt defect excitation are studied.

[0077] Based on the above analysis, the present invention is designed as follows: Figure 1 As shown, a method for identifying and locating conveyor belt surface defects based on a cleaner vibration signal comprises the following steps:

[0078] S1. Use a vibration sensor to collect the vibration signal generated when the scraper of the sweeper collides with the defect point, and use a speed sensor to collect the speed signal of the belt conveyor;

[0079] In step S1, a data acquisition card is used to synchronously acquire vibration signals and speed signals, and the data acquisition card is set to package data every 2 seconds and send it to a processing terminal.

[0080] S2. Preprocess the speed signal using a speed algorithm, and use the processing result to determine the operating status of the belt conveyor;

[0081] Step S2 specifically includes the following steps:

[0082] S21. The speed sensor uses a roller speed sensor. The roller of the roller speed sensor is fitted to the belt surface of the belt conveyor. The roller rotates through the friction between the belt and the belt. The roller rotates one circle to generate a pulse signal. The speed is obtained through two adjacent rising edges to calculate the speed V of the belt conveyor. 皮带速度 :

[0083]

[0084] In the formula, C 滚轮周长 Indicates the roller circumference of the roller speed sensor; P 索引值差值 It indicates the difference between the two horizontal coordinates corresponding to the same vertical coordinate position of two adjacent pulse signals generated by rolling two circles in sequence; f s Indicates the sampling frequency;

[0085] In step S21, the roller diameter R is set to 0.15 m, then C 滚轮周长 =πR≈0.47m.

[0086] S22. Determine the operating status of the belt conveyor based on the two sets of speed values ​​calculated in step S21.

[0087] In step S22, the two adjacent groups of speed data are set as V x and V x+1 , and set the judgment logic as follows:

[0088] When V x = 0 and V x+1 =0, it is determined that the belt conveyor is in a stopped state;

[0089] When V x = 0 and V x+1 When >0, it is determined that the belt conveyor is in the start state;

[0090] When V x >0 and V x+1 When >0, it is determined that the belt conveyor is in acceleration, constant speed or deceleration state;

[0091] When V x >0 and V x+1 =0, it is determined that the belt conveyor is in the shutdown state.

[0092] S3, identifying the defective point by combining the running state of the belt conveyor determined in step S2 and the vibration signal and speed signal collected in step S1;

[0093] Step S3 specifically includes the following steps:

[0094] S31. Based on the running status of the belt conveyor, select the following processing logic:

[0095] When the belt conveyor is in a stopped state, the vibration sensor, speed sensor, data acquisition card and processing terminal all enter a dormant state;

[0096] When the belt conveyor is in the on state, the processing terminal clears the data and waits for the distance calculation to be retriggered;

[0097] When the belt conveyor is in an acceleration, constant speed or deceleration state, it is determined whether there is a vibration signal. If so, the distance calculation is triggered and step S32 is executed. Otherwise, no action is taken.

[0098] When the belt conveyor is in shutdown state, the processing terminal stops calculating and displays the distance calculation result;

[0099] S32, using VMD algorithm to reduce noise on vibration signal and speed signal;

[0100] S321. Assuming that the original signal is s(t), the optimization problem of the VMD method is expressed as:

[0101]

[0102] In the formula, u k (t) represents the kth intrinsic mode function, ω k represents the frequency of the kth intrinsic mode function, k represents the number of intrinsic mode functions, and λ is the regularization parameter;

[0103] S322, initialization frequency ω k and the intrinsic mode function u k (t);

[0104] S323, iteratively update each intrinsic mode function u through alternating optimization k (t) and frequency ω k , until the stopping condition is met;

[0105] S324, reconstructing the signal using the decomposed intrinsic mode function and the corresponding frequency to achieve signal noise reduction;

[0106] S33, determine whether there is a defect point: take the kurtosis value of the belt conveyor during normal operation as the first threshold, calculate the kurtosis value of the vibration data after VMD denoising, and then calculate the deviation between the kurtosis value of the vibration data after denoising and the first threshold. If the deviation is greater than the set value, it is determined that the defect point exists and execute step S34. Otherwise, no action is taken to prevent the peak points from being too close and causing excessive calculation burden.

[0107] In step S33, the kurtosis value (Kurtosis is a statistic used to describe the sharpness of the probability distribution shape) of the denoised vibration data is calculated as follows:

[0108]

[0109] Where, X i represents the corresponding data point, represents the mean value of the vibration data after noise elimination, and N represents the total number of data points (number of samples).

[0110] S34, peak extraction:

[0111]

[0112] N z =t z f s (5);

[0113] Where, t is the sampling time of a set of vibration data after noise elimination; N is the number of samples of vibration data after noise elimination; t z is the critical sampling time; N z is the critical error sampling number; V 额 is the rated working speed of the belt conveyor; Z is the maximum allowable error in meters. In this embodiment, Z=2m;

[0114] In step S34, when the peak is extracted, the horizontal and vertical coordinate values ​​corresponding to the peak are saved simultaneously, wherein the horizontal coordinate value is used to calculate the time when the peak point appears, and it is set that when the interval between two peak points is less than a critical error sampling number interval, the previous peak point is extracted for calculation.

[0115] S35. Calculate the critical error sampling number N z The ratio k to the number of samples N of the vibration data after noise elimination z :

[0116]

[0117] S36. There may be multiple defect points in a set of data, that is, multiple peak points. Therefore, k peak points are taken from each set of vibration data after noise elimination, and the calculation formula of the peak point k is as follows:

[0118] k=[k z ]+1 (7);

[0119] In this embodiment, k is greater than k z An integer.

[0120] S37. Consider the extracted peak point as a defect point.

[0121] S4, using a distance algorithm to calculate the relative distance between the defect point identified in step S3 and the scraper;

[0122] Step S4 specifically includes the following steps:

[0123] S41, set the scraper as the calculation starting point, and sum the distance traveled by the defect point within the sampling time of each set of noise-removed vibration data:

[0124]

[0125] Where, distance 总 is the relative distance between the defect point and the scraper; N 1 is the index value when the peak point appears; NN 1 Indicates the number of samples that have been sampled within the critical error sampling interval when the peak value appears; V 1 V is the speed value collected in the critical error sampling interval when the peak point is collected; 2 V is the speed value collected in a critical error sampling interval after the peak point is collected; k is the speed value collected in the kth critical error sampling interval after the peak point is collected. In this embodiment, the speed value V collected in the k+1th critical error sampling interval after the peak point is collected k+1 =0;

[0126] S42. Set the total length of one circle of the belt conveyor as the second threshold value. If the calculated distance traveled by the defective point is not less than the second threshold value, the distance calculation of this defective point is eliminated. Otherwise, the belt conveyor enters the next circle. In the next circle, if the defective point is scraped off by the scraper, no action is taken. Otherwise, the distance calculation is triggered again to reduce the cumulative error and make the result more accurate.

[0127] S5. Output and display the calculation result of step S4.

[0128] Simulation experiment

[0129] In this simulation experiment, a vibration sensor is installed on the scraper spindle of the cleaner, a speed sensor is installed on the belt, and metal defects, rubber defects, and metal-rubber combination defects are installed on the belt of the tested belt conveyor. The rated operating speed V of the tested belt conveyor is 额 =2m / s, sampling frequency f s =10000Hz, sampling time t=20s, belt length 40m, sampling 20s is enough to sample a full circle, and the number of samples N=20000, the critical number of samples N z =10000, and k=3 is calculated from formulas (2) to (6), that is, at most 3 defect points are taken in one sampling interval.

[0130] The result is as follows Figure 2-Figure 11 As shown, a total of 11 defect points were detected, and the minimum defect point amplitude was 0.041.

[0131] Table 1 Comparison between the actual stop position of the defect point and the calculated position of the present invention

[0132] Fault number 1 2 3 4 5 6 7 8 9 Physical location scrape 4.7m 7.7m 12.5m 16.2m 21.0m 26.4m 31.5m 36.5m Test location 2.38m 4.62m 7.66m 11.43m 15.92m 20.88m 25.84m 30.8m 35.76m Error value Accidental touch 0.08m 0.04m 1.07m 0.28m 0.12m 0.56m 0.7m 0.74m

[0133] As can be seen from Table 1, the present invention can detect 8 out of 9 fault points, and the errors are all within the allowable range of 2m, thereby confirming the effectiveness of the present invention.

[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for identifying and locating conveyor belt surface defects based on cleaner vibration signals, characterized in that: The following steps are involved: S1. Use a vibration sensor to collect the vibration signal generated when the scraper of the sweeper collides with the defect point, and use a speed sensor to collect the speed signal of the belt conveyor; S2. Preprocess the speed signal using a speed algorithm, and use the processing result to determine the operating status of the belt conveyor; S3, identifying the defective point by combining the running state of the belt conveyor determined in step S2 and the vibration signal and speed signal collected in step S1; S4, using a distance algorithm to calculate the relative distance between the defect point identified in step S3 and the scraper; S5. Output and display the calculation result of step S4.

2. The method for identifying and locating conveyor belt surface defects based on cleaner vibration signals according to claim 1 is characterized in that: In step S1, a data acquisition card is used to synchronously acquire vibration signals and speed signals, and the data acquisition card is set to package data every 2 seconds and send it to a processing terminal.

3. The method for identifying and locating conveyor belt surface defects based on cleaner vibration signals according to claim 2 is characterized in that: Step S2 specifically includes the following steps: S21. The speed sensor uses a roller speed sensor. The roller of the roller speed sensor is fitted to the belt surface of the belt conveyor. The roller rotates through the friction between the roller and the belt. A pulse signal is generated when the roller rotates one circle. The speed V of the belt conveyor is calculated through two adjacent rising edges. 皮带速度 : In the formula, C 滚轮周长 Indicates the roller circumference of the roller speed sensor; P 索引值差值 It indicates the difference between the two horizontal coordinates corresponding to the same vertical coordinate position of two adjacent pulse signals generated by rolling two circles in sequence; f s Indicates the sampling frequency; S22. Determine the operating status of the belt conveyor based on the two sets of speed values ​​calculated in step S21.

4. The method for identifying and locating conveyor belt surface defects based on cleaner vibration signals according to claim 3 is characterized in that: In step S21, the roller diameter R is set to 0.15 m, then C 滚轮周长 =πR≈0.47m.

5. The method for identifying and locating conveyor belt surface defects based on cleaner vibration signals according to claim 4 is characterized in that: In step S22, the two adjacent groups of speed data are set as V x and V x+1 , and set the judgment logic as follows: When V x = 0 and V x+1 =0, it is determined that the belt conveyor is in a stopped state; When V x = 0 and V x+1 When >0, it is determined that the belt conveyor is in the start state; When V x >0 and V x+1 When >0, it is determined that the belt conveyor is in acceleration, constant speed or deceleration state; When V x >0 and V x+1 =0, it is determined that the belt conveyor is in the shutdown state.

6. The method for identifying and locating conveyor belt surface defects based on cleaner vibration signals according to claim 5 is characterized in that: Step S3 specifically includes the following steps: S31. Based on the running status of the belt conveyor, select the following processing logic: When the belt conveyor is in a stopped state, the vibration sensor, speed sensor, data acquisition card and processing terminal all enter a dormant state; When the belt conveyor is in the on state, the processing terminal clears the data and waits for the distance calculation to be retriggered; When the belt conveyor is in an acceleration, constant speed or deceleration state, it is determined whether there is a vibration signal. If so, the distance calculation is triggered and step S32 is executed. Otherwise, no action is taken. When the belt conveyor is in shutdown state, the processing terminal stops calculating and displays the distance calculation result; S32, using VMD algorithm to reduce noise on vibration signal and speed signal; S321. Assuming that the original signal is s(t), the optimization problem of the VMD method is expressed as: In the formula, u k (t) represents the kth intrinsic mode function, ω k represents the frequency of the kth intrinsic mode function, k represents the number of intrinsic mode functions, and λ is the regularization parameter; S322, initialization frequency ω k and the intrinsic mode function u k (t); S323, iteratively update each intrinsic mode function u through alternating optimization k (t) and frequency ω k , until the stopping condition is met; S324, reconstructing the signal using the decomposed intrinsic mode function and the corresponding frequency to achieve signal noise reduction; S33, determine whether there is a defect point: take the kurtosis value of the belt conveyor during normal operation as the first threshold, calculate the kurtosis value of the vibration data after VMD denoising, and then calculate the deviation between the kurtosis value of the vibration data after denoising and the first threshold. If the deviation is greater than the set value, it is determined that the defect point exists, and step S34 is executed, otherwise no action is taken; S34, peak extraction: N z =t z f s (5); Where, t is the sampling time of vibration data; N is the number of samples of vibration data after noise elimination; t z is the critical sampling time; N z is the critical error sampling number; V 额 is the rated working speed of the belt conveyor; Z is the maximum allowable error in meters; S35. Calculate the critical error sampling number N z The ratio k to the number of samples N of the vibration data after noise elimination z : S36. Take k peak points from each set of vibration data after noise elimination, and the calculation formula of the peak point k is as follows: k=[k z ]+1 (7); S37. Consider the extracted peak point as a defect point.

7. The method for identifying and locating conveyor belt surface defects based on cleaner vibration signals according to claim 6, characterized in that: In step S33, the kurtosis value of the vibration data after noise elimination is calculated as follows: Where, X i represents the corresponding data point, represents the mean value of the vibration data after noise elimination, and N represents the total number of data points.

8. The method for identifying and locating conveyor belt surface defects based on cleaner vibration signals according to claim 7 is characterized in that: In step S34, when the peak value is extracted, the horizontal and vertical coordinate values ​​corresponding to the peak value are saved simultaneously, and it is set that when the interval between two peak points is less than a critical error sampling number interval, the previous peak point is extracted for calculation.

9. The method for identifying and locating conveyor belt surface defects based on cleaner vibration signals according to claim 8, characterized in that: Step S4 The specific steps include: S41, set the scraper as the calculation starting point, and sum the distance traveled by the defect point within the sampling time of each group of noise-removed vibration data: Where, distance 总 is the relative distance between the defect point and the scraper; N1 is the index value when the peak point appears; N-N1 represents the number of samples taken within the critical error sampling number interval when the peak point appears; V1 is the speed value sampled in the critical error sampling number interval when the peak point is collected; V2 is the speed value sampled in the critical error sampling number interval after the peak point is collected; V k is the speed value collected in the kth critical error sampling interval after the peak point is collected; S42. Set the total length of one circle of the belt conveyor as the second threshold value. If the calculated distance traveled by the defective point is not less than the second threshold value, the distance calculation of this defective point is eliminated. Otherwise, the belt conveyor enters the next circle. In the next circle, if the defective point is scraped off by the scraper, no action is taken. Otherwise, recalculation of the distance is triggered.

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