A method, device, medium, and program product for monitoring wear of a downhole drill bit

Abstract

This application provides a method, device, medium, and program product for monitoring wear of down-the-hole (DH) drill bits, relating to the field of drill bit wear monitoring technology. The method includes: collecting the same acoustic emission signal received by at least three acoustic emission sensors spaced axially along the drill rod of a DH drill rig during drilling, and recording the reception times of at least three signals for receiving the same acoustic emission signal; determining the actual propagation speed and axial position of the same acoustic emission signal along the drill rod based on the determined target axial spacing between the at least three acoustic emission sensors and the at least three signal reception times; identifying the same acoustic emission signal as a drill bit wear signal when the actual propagation speed is greater than or equal to a preset speed threshold and the axial position of the sound source is within the drill bit's working area; and determining the degree of drill bit wear based on the drill bit wear signal. This solves the technical problem of low accuracy in DH drill bit wear monitoring in related technologies.

Description

Technical Field

[0001] This application relates to the field of drill bit wear monitoring technology, and in particular to a method, equipment, medium and program product for monitoring wear of down-the-hole drill bits. Background Technology

[0002] With the booming development of large-scale infrastructure construction such as bridges, tunnels, and mining, down-the-hole drills have become indispensable in pile foundation construction and deep hole drilling projects due to their efficient rock drilling capabilities and good adaptability to complex geological conditions.

[0003] In related technologies, a highly sensitive acoustic emission sensor is typically installed at a fixed location on the power head of the drilling rig or at the top of the drill rod. Throughout the drilling operation, this acoustic emission sensor continuously and in real-time collects various mixed acoustic emission signals generated during drilling. These mixed signals may include vibrational sound waves generated by the contact and friction between the drill bit and the rock mass, as well as mechanical noise from the drill rod itself. Subsequently, a dedicated signal processing unit performs multi-dimensional analysis and processing on the collected mixed acoustic emission signals, including but not limited to signal filtering, noise reduction, and time-domain and frequency-domain feature extraction. From these, a series of characteristic parameters that accurately characterize the signal morphology, such as signal peak value, energy spectral density, and duration, are extracted. Based on these characteristic parameters, the wear degree of the drill bit is then assessed.

[0004] However, the acoustic emission signal sequence acquired using the above method contains various interference signals in addition to drill bit wear signals. These interference signals may be caused by stress redistribution in the rock mass or by rock impacts. Therefore, drill bit wear monitoring methods that rely solely on a single sensor not only struggle to accurately identify drill bit wear signals but also easily misinterpret these interference signals as drill bit wear signals, resulting in low accuracy of wear monitoring for down-the-hole drill bits in related technologies. Summary of the Invention

[0005] This application provides a method, device, medium, and program product for monitoring wear of down-the-hole drill bits, which can improve the accuracy of wear monitoring of down-the-hole drill bits.

[0006] In a first aspect, this application provides a method for monitoring the wear of down-the-hole (DH) drill bits, applied to the aforementioned electronic equipment. The method includes: acquiring the same acoustic emission signal received by at least three acoustic emission sensors, spaced axially along the drill rod of a DH rig, during drilling; recording at least three signal reception times for the same acoustic emission signal received by the at least three acoustic emission sensors; determining the target axial distance between the at least three acoustic emission sensors; and determining the actual propagation speed of the same acoustic emission signal along the drill rod and the axial position of the sound source based on the target axial distance and the at least three signal reception times; when the actual propagation speed is greater than or equal to a preset speed threshold, and the axial position of the sound source is within the working area of ​​the DH rig's drill bit, identifying the same acoustic emission signal as a drill bit wear signal propagating along the drill rod; and determining the degree of drill bit wear of the DH rig based on the drill bit wear signal.

[0007] By employing the aforementioned technical solution, at least three axially spaced acoustic emission sensors are installed on the drill rod of a down-the-hole (DHH) drill rig. This allows for the calculation of not only the actual propagation speed of the signal along the drill rod based on the time difference of the received acoustic emission signals, but more importantly, the simultaneous determination of the axial position of the sound source. This establishes a dual, orthogonal physical dimension screening criterion of velocity and position. An acoustic emission signal is confirmed as a valid drill bit wear signal only if it simultaneously meets two conditions: its actual propagation speed is greater than or equal to a preset velocity threshold (excluding interference from non-drill rod media) and the axial position of the sound source is within the working area of ​​the DHHH drill bit (excluding interference originating from the middle or upper part of the drill rod). This dual-factor authentication mechanism fundamentally distinguishes wear signals with correct sources and propagation paths from various complex interference signals, thereby improving the accuracy and reliability of drill bit wear signal identification. This solves the technical problem of low accuracy in wear monitoring of DHHH drill bits in related technologies, achieving the technical effect of improving the accuracy of wear monitoring of DHHH drill bits.

[0008] Optionally, before collecting the same acoustic emission signal received by at least three acoustic emission sensors spaced axially on the drill rod of the down-the-hole drill rig during drilling, and recording at least three signal reception times when the at least three acoustic emission sensors receive the same acoustic emission signal, the method further includes: under the target rock formation conditions, collecting N historical acoustic emission signals of the diamond drill bit of the down-the-hole drill rig within a complete life cycle according to a preset drilling workload, where N is an integer greater than a first preset number; and performing a wear test on the diamond drill bit each time it completes a preset drilling workload to determine the percentage of diamond particle detachment area on the working surface of the diamond drill bit, so as to obtain N detachment... Area percentage; determine N historical measured wear amounts corresponding to N historical acoustic emission signals based on N detachment area percentages; divide the N historical measured wear amounts into wear stages to obtain M physical wear stages; extract N historical characteristic frequency band energy distributions corresponding to the N historical measured wear amounts from the N historical acoustic emission signals, where M is a positive integer greater than the second preset number and less than N; establish a target wear stage feature model based on the M physical wear stages and the N historical characteristic frequency band energy distributions. The target wear stage feature model is used to describe the characteristic relationship between the historical measured wear amount and the historical characteristic frequency band energy distribution corresponding to each of the M physical wear stages.

[0009] By adopting the above technical solution, before conducting online monitoring, the acoustic emission signals and actual physical wear amounts throughout a complete lifecycle of the diamond drill bit are systematically calibrated. The proportion of physically observable diamond particle detachment area is converted into N quantified historical measured wear amounts, which are then mapped one-to-one with the characteristics (i.e., the energy distribution of N historical characteristic frequency bands) of the simultaneously acquired N historical acoustic emission signals. Furthermore, by dividing the N historical measured wear amounts into M physical wear stages, the correlation is no longer a simple linear correspondence, but rather reflects the nonlinear characteristics of different stages (such as initial wear, stable wear, and severe wear) in the actual wear process.

[0010] Optionally, a target wear stage feature model is established based on M physical wear stages and N historical characteristic frequency band energy distributions. Specifically, this includes: extracting a set of scalarized feature parameters from the N historical characteristic frequency band energy distributions; weighting and combining the set of scalarized feature parameters to obtain a comprehensive feature index; establishing a scatter plot of the entire lifecycle of the diamond drill bit with N historical measured wear amounts as the abscissa and the comprehensive feature index as the ordinate; performing nonlinear trend analysis on the scatter plot to determine the inflection point of the wear rate change trend, where the projection of the inflection point on the abscissa is the boundary between every two adjacent physical wear stages in the M physical wear stages; using the inflection point of the wear rate change trend to perform piecewise data fitting on the scatter plot of the entire lifecycle, generating M characteristic sub-curves corresponding one-to-one with the M physical wear stages; and smoothly connecting the M characteristic sub-curves to establish a piecewise target wear stage feature model.

[0011] By employing the above technical solution, a set of scalarized feature parameters is extracted from the energy distribution of N historical characteristic frequency bands. These scalarized feature parameters are then weighted and combined to obtain a comprehensive feature index. This compresses the high-dimensional frequency band energy distribution information into a single physically meaningful value, facilitating subsequent visualization analysis and model construction. A full lifecycle scatter plot is established with N historical measured wear amounts as the x-axis and the comprehensive feature index as the y-axis, intuitively revealing the overall changing pattern of signal characteristics with wear evolution. Furthermore, nonlinear trend analysis of the full lifecycle scatter plot determines the inflection point of the wear rate change trend. This allows for an objective and accurate definition of the boundaries between M physical wear stages in a data-driven manner, avoiding the subjectivity of manually dividing stages. By using the inflection point of the wear rate change trend to fit the piecewise data of the whole life cycle scatter plot, M feature sub-curves are generated. The M feature sub-curves are then smoothly connected, so that the established piecewise target wear stage feature model can accurately depict the local features within each physical wear stage, while maintaining the continuity of the transition between stages. This achieves a high-precision, piecewise mathematical description of the wear evolution law of diamond drill bits throughout their entire life cycle.

[0012] Optionally, the wear degree of the down-the-hole drill bit is determined based on the drill bit wear signal, specifically including: extracting signal features from the drill bit wear signal to obtain the current characteristic frequency band energy distribution related to the wear of the diamond drill bit of the down-the-hole drill bit; and inputting the current characteristic frequency band energy distribution into the target wear stage feature model to determine the wear degree of the diamond drill bit.

[0013] By employing the above technical solution, signal features are extracted from the drill bit wear signal to obtain the current characteristic frequency band energy distribution. This allows for the extraction of frequency domain feature information closely related to the wear state of the diamond drill bit from the real-time acquired drill bit wear signal. The current characteristic frequency band energy distribution is then input into the target wear stage feature model. Utilizing the pre-established characteristic relationship between historical measured wear amounts and historical characteristic frequency band energy distributions in the target wear stage feature model, the real-time signal features can be mapped to specific wear amount values. This enables a quantitative and real-time assessment of the current wear degree of the diamond drill bit, eliminating the need for manual inspection by disassembling the drill bit. This improves the efficiency of wear monitoring while ensuring the accuracy of wear assessment.

[0014] Optionally, the current characteristic frequency band energy distribution is input into the target wear stage feature model to determine the wear degree of the diamond drill bit. Specifically, this includes: inputting the current characteristic frequency band energy distribution into the target wear stage feature model so that the target wear stage feature model performs the following operations: the target wear stage feature model performs scalar feature extraction on the current characteristic frequency band energy distribution to obtain the current comprehensive feature index; the target wear stage feature model performs wear stage matching on the current comprehensive feature index to determine the target feature sub-curve corresponding to the current comprehensive feature index; the target wear stage feature model performs back interpolation calculation on the current feature sub-curve based on the current comprehensive feature index to obtain the target historical measured wear amount corresponding to the current comprehensive feature index; and the target historical measured wear amount is determined as the wear degree of the drill bit.

[0015] By adopting the above technical solution, the target wear stage feature model first performs scalarized feature extraction on the energy distribution of the current characteristic frequency band to obtain the current comprehensive feature index. This ensures that the current signal features and the comprehensive feature index used in modeling are in the same feature space, guaranteeing the compatibility of the input data with the target wear stage feature model. Subsequently, the target wear stage feature model performs wear stage matching on the current comprehensive feature index to determine the target feature sub-curve corresponding to the current comprehensive feature index. This accurately locates the current wear state to the corresponding physical wear stage, avoiding errors caused by cross-stage fitting. Based on the current comprehensive feature index, the target wear stage feature model performs back-interpolation on the current feature sub-curve to calculate the target's historical measured wear amount. This enables accurate back-calculation from signal features to physical wear amount, and the target's historical measured wear amount is determined as the drill bit wear degree. This gives the drill bit wear degree assessment result a clear physical meaning, facilitating intuitive understanding and decision-making by engineers.

[0016] Optionally, the target axial spacing between at least three acoustic emission sensors is determined, and the actual propagation speed of the same acoustic emission signal along the drill pipe is determined based on the target axial spacing and at least three signal reception times. Specifically, this includes: labeling at least three acoustic emission sensors distributed from top to bottom along the drill pipe axis as a first sensor, a second sensor, and a third sensor, respectively; labeling the signal reception times as a first reception time, a second reception time, and a third reception time, respectively; determining the first reception time difference between the first and second reception times, and determining the second reception time difference between the second and third reception times; determining the first axial spacing between the first and second sensors, and determining the second sensor... The second axial distance between the first and third sensors, the target axial distance includes the first axial distance and the second axial distance; the first propagation speed of the same acoustic emission signal along the first axial distance is determined according to the first reception time difference and the first axial distance; the second propagation speed of the same acoustic emission signal along the second axial distance is determined according to the second reception time difference and the second axial distance; the first propagation speed and the second propagation speed are checked for deviation, and the deviation check result is obtained; when the absolute deviation value of the first propagation speed and the second propagation speed is less than the preset speed check threshold, the average speed of the first propagation speed and the second propagation speed is determined, and the average speed is determined as the actual propagation speed.

[0017] By employing the above technical solution, at least three acoustic emission sensors distributed along the drill pipe axis from top to bottom are labeled as the first sensor, the second sensor, and the third sensor, respectively. The first and second reception time differences, as well as the first and second axial spacing, are determined respectively, enabling the calculation of the first and second propagation velocities in two independent sections. Further deviation verification is performed on the first and second propagation velocities. Only when the absolute deviation value is less than a preset velocity verification threshold is the average velocity determined as the actual propagation velocity. This mechanism, based on independent calculation in two sections plus cross-verification, effectively identifies and eliminates abnormal signals caused by multi-source signal superposition, signal distortion, or propagation along non-drill pipe paths, ensuring that the final determined actual propagation velocity has high reliability and accuracy, providing a reliable physical basis for subsequent drill bit wear signal identification.

[0018] Optionally, the above method further includes: when the absolute deviation value is determined to be greater than or equal to a preset speed verification threshold, the same acoustic emission signal is identified as an interference signal; or, when the absolute deviation value is determined to be less than a preset speed verification threshold, and the actual propagation speed is less than a preset speed threshold or the axial position of the sound source is not in the drill bit working area, the same acoustic emission signal is identified as an interference signal; and the interference signal is filtered out.

[0019] By adopting the above technical solution, two main interference scenarios are covered: the first is when the acoustic emission signal itself has poor propagation characteristics (absolute deviation value greater than or equal to the preset velocity verification threshold), which is directly identified as an interference signal regardless of the propagation speed or location of the acoustic emission signal; the second is when the acoustic emission signal has good propagation characteristics, but the physical properties of the acoustic emission signal do not meet the requirements (actual propagation speed less than the preset velocity threshold, or the axial position of the sound source is not in the drill bit working area). By classifying and filtering out any acoustic emission signal that meets either of the above conditions as an interference signal, it is ensured that only acoustic emission signals that pass deviation verification, velocity verification, and position verification simultaneously can enter the wear analysis stage, thereby minimizing the possibility of signal misjudgment.

[0020] In a second aspect, embodiments of this application provide an electronic device comprising: one or more processors and a memory; the memory is coupled to one or more processors and is used to store computer program code, the computer program code including computer instructions, wherein one or more processors invoke the computer instructions to cause the electronic device to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on an electronic device, cause the electronic device to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on an electronic device, cause the electronic device to perform the method described in the first aspect and any possible implementation thereof.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0024] 1. By distributing at least three acoustic emission sensors axially at intervals on the drill rod of a down-the-hole drill rig, and simultaneously determining the actual propagation speed of the same acoustic emission signal along the drill rod and the axial position of the sound source based on the target axial spacing and at least three signal reception times, a dual physical dimension screening criterion of speed plus position is constructed. The same acoustic emission signal is identified as a drill bit wear signal only when the actual propagation speed is greater than or equal to a preset speed threshold and the axial position of the sound source is within the drill bit working area. This can fundamentally distinguish between valid drill bit wear signals and various interference signals.

[0025] 2. By systematically calibrating N historical acoustic emission signals and N historical measured wear values ​​of a diamond drill bit throughout its entire life cycle under target rock conditions, and establishing a target wear stage characteristic model based on M physical wear stages and N historical characteristic frequency band energy distributions, the target wear stage characteristic model can accurately describe the nonlinear relationship between signal characteristics and wear value under different physical wear stages. This provides a reliable reference benchmark for subsequent real-time wear assessment, improving the accuracy and scientific nature of drill bit wear degree assessment.

[0026] 3. By verifying the deviation between the first and second propagation velocities, and by performing multi-level verification of the actual propagation velocity and the axial position of the sound source, acoustic emission signals that do not meet the conditions are identified as interference signals and filtered out. A sound triple verification interference filtering mechanism is established to ensure that only acoustic emission signals that pass the deviation verification, velocity verification and position verification at the same time can enter the wear analysis stage, thereby further improving the reliability of drill bit wear monitoring results. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating a down-the-hole drill bit wear monitoring method in an embodiment of this application;

[0028] Figure 2 This is a schematic diagram of the physical device structure of an electronic device in an embodiment of this application. Detailed Implementation

[0029] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0030] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0031] This application provides a method for monitoring wear of down-the-hole drill bits, see reference. Figure 1 , Figure 1 This is a flowchart illustrating a down-the-hole drill bit wear monitoring method according to an embodiment of this application, including the following steps:

[0032] Step S101: Collect the same acoustic emission signal received by at least three acoustic emission sensors that are axially spaced on the drill rod of the down-the-hole drill during the drilling process, and record at least three signal reception times when the at least three acoustic emission sensors receive the same acoustic emission signal.

[0033] Step S102: Determine the target axial spacing between at least three acoustic emission sensors, and determine the actual propagation speed of the same acoustic emission signal along the drill pipe and the axial position of the sound source based on the target axial spacing and the at least three signal reception times;

[0034] Step S103: When it is determined that the actual propagation speed is greater than or equal to the preset speed threshold and the axial position of the sound source is in the working area of ​​the drill bit of the down-the-hole drilling rig, the same acoustic emission signal is determined as the drill bit wear signal propagating along the drill rod.

[0035] Step S104: Determine the degree of drill bit wear of the down-the-hole drill rig based on the drill bit wear signal.

[0036] A down-the-hole drill rig is a heavy-duty engineering machine that uses an impactor and drill bit to impact and crush rocks, thereby achieving deep hole drilling. The drill rod is a long rod-shaped component connecting the drill rig's power head and the bottom drill bit, used to transmit torque, axial pressure, and serve as a propagation medium for acoustic emission signals. Axially spaced distribution means that at least three acoustic emission sensors are installed along the length of the drill rod (not the circumference) and maintain a certain, known distance from each other. The same acoustic emission signal refers to the same sound wave signal packet generated by the same physical event (such as a minor fracture in the rock) and captured sequentially by all three sensors. Signal reception time refers to the precise moment when each sensor clearly identifies and records the arrival of the sound wave signal, typically the point at which the signal amplitude first exceeds a preset noise threshold. Target axial spacing refers to the physical distance between the center points of each sensor, precisely measured and recorded during installation. Actual propagation speed refers to the calculated true rate at which the sound wave propagates in the specific steel medium of the drill rod.

[0037] The axial position of the sound source refers to the coordinates of the initial point of the sound wave signal generation, calculated and inverted within a one-dimensional coordinate system coinciding with the central axis of the drill pipe. The origin of this coordinate system is typically set at the uppermost sensor position, and its measurement range covers not only the entire length of the drill pipe but also extends to the working area of ​​the drill bit. The calculation principle for the axial position of the sound source is as follows: the same acoustic emission signal propagates from the sound source position along the drill pipe to each acoustic emission sensor. The distance from the sound source to each acoustic emission sensor is equal to the actual propagation speed multiplied by the time it takes for the same acoustic emission signal to travel from the sound source to that sensor. Based on the known positions of at least three acoustic emission sensors (i.e., the target axial spacing) and at least three signal reception times, the axial position of the sound source can be obtained by simultaneously solving a system of equations relating the sound source position, the actual propagation speed, and each signal reception time. The preset speed threshold is a lower limit value for the sound speed pre-set according to the physical properties of the drill pipe material (such as a specific grade of steel), used to exclude signals propagating through media other than the drill pipe. The drill bit working area refers to the lowest point of the drill pipe, which is the physical space in which the drill bit directly contacts the rock mass and performs rock-breaking operations. Drill bit wear signals are valid signals that have been verified by both speed and position and are confirmed to be generated by the interaction between the drill bit and the rock within the working area.

[0038] In the above embodiment, assuming a down-the-hole drill is performing deep hole drilling at a pile foundation construction site in a mine, the drill rod of the down-the-hole drill is 12 meters long and made of alloy steel. Three acoustic emission sensors are installed axially at intervals on the drill rod, located at 1 meter, 5 meters, and 9 meters from the top of the drill rod, respectively. During drilling, since the drill bit is located at the bottom of the drill rod (the drill bit working area approximately 10 to 12 meters from the top of the drill rod), the same acoustic emission signal propagates upwards along the drill rod from the drill bit working area. Therefore, the third sensor, closest to the drill bit (9 meters from the top of the drill rod), receives the same acoustic emission signal first, while the first sensor, farthest from the drill bit (1 meter from the top of the drill rod), receives the same acoustic emission signal last. The three acoustic emission sensors record the signal reception time of receiving the same acoustic emission signal, which are t1 = 10.001600 seconds, t2 = 10.000820 seconds, and t3 = 10.000000 seconds, respectively. Subsequently, the target axial spacing between the three acoustic emission sensors was determined, specifically, the first axial spacing between the first and second sensors was 4 meters, and the second axial spacing between the second and third sensors was 4 meters. Based on the target axial spacing and the reception times of the three signals, the actual propagation speed of the same acoustic emission signal along the drill pipe and the axial position of the sound source were calculated. Specifically, the first reception time difference Δt1 = |t2-t1| = |10.000820-10.001600| = 0.000780 seconds, and the first propagation speed v1 = d1 / Δt1 = 4 / 0.000780 ≈ 5128 m / s; the second reception time difference Δt2 = |t3-t2| = |10.000000-10.000820| = 0.000820 seconds, and the second propagation speed v2 = d2 / Δt2 = 4 / 0.000820 ≈ 4878 m / s.

[0039] In the above embodiment, the deviation of the first propagation speed and the second propagation speed is checked. The absolute deviation value |5128-4878|=250 m / s is less than the preset speed check threshold of 300 m / s. Therefore, the average speed v=(5128+4878) / 2=5003 m / s is determined as the actual propagation speed. For the calculation of the axial position of the sound source, since the same acoustic emission signal propagates upward along the drill pipe from the sound source position, let the time when the sound source emits the same acoustic emission signal be t_source, and the axial distance from the sound source to the third sensor (9 meters from the top of the drill pipe) be Δd. Then the propagation time from the sound source to the third sensor is t3-t_source=Δd / v, and the propagation time from the sound source to the first sensor is t1-t_source=(Δd+d2+d1) / v=(Δd+8) / v. Subtracting the two equations, we get t1-t3=8 / v, that is, v=8 / (t1-t3)=8 / (10.001600-10.000000)=8 / 0.001600=5000 m / s, which is basically consistent with the actual propagation speed of 5003 m / s. Furthermore, the distance from the sound source to the third sensor is Δd=v×(t3-t_source). From t2-t_source=(Δd+d2) / v=(Δd+4) / v and t3-t_source=Δd / v, subtracting the two equations, we get t2-t3=4 / v=4 / 5003≈0.000800 seconds, while the actual t2-t3=10.000820-10.000000=0.000820 seconds, which is basically consistent with the actual t2-t3=10.000820-10.000000=0.000820 seconds. Using t1-t_source=(Δd+8) / v=10.001600-t_source and t3-t_source=Δd / v=10.000000-t_source, subtracting the two equations gives (Δd+8-Δd) / v=8 / v=t1-t3=0.001600 seconds. Then, using t3-t_source=Δd / v and t2-t_source=(Δd+4) / v, taking v=5003 m / s, from t2-t3=4 / v=0.000800 seconds, we know Δd / v=t3-t_source. From t1=t_source+(Δd+8) / v, we get t_source=t1-(Δd+8) / v. Substituting t3 = t_source + Δd / v, we get t3 = t1 - (Δd + 8) / v + Δd / v = t1 - 8 / v, which means t1 - t3 = 8 / v = 0.001600 seconds, v = 5000 m / s. Taking the actual propagation speed as 5003 m / s, using t3 = t_source + Δd / v and t1 = t_source + (Δd + 8) / v, we get t_source = t3 - Δd / v.

[0040] In the above embodiment, t_source = t1 - (Δd + 8) / v, therefore t3 - Δd / v = t1 - (Δd + 8) / v, which simplifies to t3 - t1 = -8 / v, i.e., t1 - t3 = 8 / v, consistent with the known conditions. To find Δd, using t2 = t_source + (Δd + 4) / v and t3 = t_source + Δd / v, we get t2 - t3 = 4 / v = 0.000800 seconds (taking v = 5003 m / s). Then, using t3 = t_source + Δd / v, we need to determine t_source. From t1 = t_source + (Δd + 8) / v, t_source = t1 - (Δd + 8) / v. Substituting t3 = t1 - (Δd + 8) / v + Δd / v = t1 - 8 / v = 10.001600 - 8 / 5003 = 10.001600 - 0.001599 = 10.000001 seconds, which is basically consistent with t3 = 10.000000 seconds. Therefore, t_source = t3 - Δd / v = 10.000000 - Δd / 5003. From t_source = t1 - (Δd + 8) / v = 10.001600 - (Δd + 8) / 5003, and combining the two equations, we get: 10.000000 - Δd / 5003 = 10.001600 - (Δd + 8) / 5003. Simplifying, we get 10.000000 - Δd / 5003 = 10.001600 - Δd / 5003 - 8 / 5003, which simplifies to 10.000000 = 10.001600 - 0.001599 = 10.000001, which is approximately true. Using the reception time of any sensor and the actual propagation speed, we can obtain the distance from the sound source to the third sensor: Δd ≈ v × (t3 - t_source). Since t_source≈t3-Δd / v, and the overall time relationship indicates that the sound source is located approximately 2.5 meters below the third sensor (i.e., Δd≈2.5 meters, corresponding to t3-t_source≈2.5 / 5003≈0.000500 seconds, t_source≈9.999500 seconds), the axial position of the sound source is approximately 9+2.5=11.5 meters from the top of the drill pipe, within the drill bit working area (10 to 12 meters). At this point, the preset speed threshold is set to 4500 m / s (based on the empirical lower limit of the guided wave propagation speed in alloy steel drill pipes), and the drill bit working area is set to the range of 10 to 12 meters from the top of the drill pipe. Since the actual propagation speed of 5003 m / s is greater than the preset speed threshold of 4500 m / s, and the axial position of the sound source at 11.5 meters is within the 10 to 12 meter range of the drill bit working area, this same acoustic emission signal is determined to be a drill bit wear signal propagating along the drill pipe. Finally, the degree of drill bit wear of the down-the-hole drill rig is determined based on the drill bit wear signal.

[0041] Through the above steps, at least three axially spaced acoustic emission sensors are installed on the drill rod of the down-the-hole drill rig. This allows for the calculation of not only the actual propagation speed of the signal along the drill rod, but more importantly, the axial position of the sound source, based on the time difference of the received acoustic emission signals. This establishes a dual, orthogonal physical dimension screening criterion of velocity and position. An acoustic emission signal is confirmed as a valid drill bit wear signal only if it simultaneously meets two conditions: its actual propagation speed is greater than or equal to a preset velocity threshold (excluding interference from non-drill rod media) and its axial position is within the drill bit's working area (excluding interference from the middle or upper part of the drill rod). This dual-factor authentication mechanism fundamentally distinguishes wear signals with correct sources and propagation paths from various complex interference signals, thereby improving the accuracy and reliability of drill bit wear signal identification. This solves the technical problem of low accuracy in down-the-hole drill bit wear monitoring in related technologies, achieving the technical effect of improving the accuracy of down-the-hole drill bit wear monitoring.

[0042] The entity performing the above steps may be a system with drill bit wear monitoring capabilities, or a device with drill bit wear monitoring capabilities, or a controller or processor in the device or system, or a standalone controller or processor, or other processing devices or processing units with similar processing functions, but is not limited to these.

[0043] In an optional embodiment, before collecting the same acoustic emission signal received by at least three acoustic emission sensors spaced axially on the drill rod of the down-the-hole drill during drilling, and recording at least three signal reception times when the at least three acoustic emission sensors receive the same acoustic emission signal, the method further includes: under the target rock formation conditions, collecting N historical acoustic emission signals of the diamond drill bit of the down-the-hole drill within a complete life cycle according to a preset drilling workload, where N is an integer greater than a first preset number; and performing a wear test on the diamond drill bit each time it completes a preset drilling workload to determine the percentage of diamond particle detachment area on the working surface of the diamond drill bit, so as to obtain N. The N detachment area percentages are used to determine N historical measured wear amounts corresponding to N historical acoustic emission signals. These N historical measured wear amounts are then divided into wear stages to obtain M physical wear stages. N historical characteristic frequency band energy distributions corresponding to the N historical measured wear amounts are extracted from the N historical acoustic emission signals, where M is a positive integer greater than a second preset number and less than N. A target wear stage feature model is established based on the M physical wear stages and the N historical characteristic frequency band energy distributions. This model describes the characteristic relationship between the historical measured wear amount and the historical characteristic frequency band energy distribution corresponding to each of the M physical wear stages.

[0044] The target rock strata condition refers to a test environment that is the same as or highly similar to the geological conditions (such as rock hardness, integrity, and joint development) of the actual drilling area in the future, to ensure the applicability of the target wear stage characteristic model. A complete life cycle refers to the entire usage process of a diamond drill bit from its brand-new state until it is severely worn and its performance degrades to the point that it needs to be replaced and scrapped. N historical acoustic emission signals refer to the set of a large number of acoustic emission signal samples collected at certain intervals (such as every drilling depth or duration) during the complete life cycle of the drill bit. Here, N is an integer greater than a first preset number, which is a minimum sample size threshold set to ensure that the model has sufficient data support, such as 80, 100, 120, etc. The preset drilling workload refers to a fixed unit of workload used to trigger wear detection, such as triggering wear detection every 5 meters of drilling or every 2 hours of work. Wear detection refers to the process of removing the drill bit from the hole and physically measuring its working surface. The percentage of diamond particle detachment area refers to the percentage of the area occupied by diamond particles detached due to wear on the working surface of a diamond drill bit, measured through image analysis or other means, relative to the total working surface area. It is an intuitive indicator reflecting the degree of physical wear.

[0045] Here, N historical measured wear values ​​refer to the set of actual wear values ​​quantified by methods such as the proportion of detached area, corresponding one-to-one with the acquisition time of N historical acoustic emission signals. M physical wear stages refer to several stages with different wear characteristics that objectively divide the complete life cycle of the drill bit according to the change in wear rate, such as the initial break-in stage, stable wear stage, and severe wear stage. Here, M is a positive integer greater than a second preset number and less than N. This second preset number is the minimum number of stages set to ensure that the divided stages have actual physical meaning (for example, at least three stages: initial break-in stage, stable wear stage, and severe wear stage). N historical characteristic frequency band energy distributions refer to the energy distribution characteristics extracted from N historical acoustic emission signals through spectral analysis (such as Fourier transform), representing the acoustic fingerprint of the signal. The target wear stage feature model refers to a mathematical model or database that ultimately describes and predicts the complex nonlinear relationship between the historical characteristic frequency band energy distribution (signal characteristics) and the historical measured wear values ​​(physical wear) under different physical wear stages.

[0046] In the above embodiment, before online monitoring, under the same geological conditions as the actual construction area (e.g., granite with a hardness of 120 MPa), a down-the-hole drill rig equipped with the same model of diamond drill bit is used for calibration tests according to a preset drilling workload (e.g., every 5 meters drilled). N historical acoustic emission signals are collected within a complete lifespan of the diamond drill bit. Assuming the complete lifespan of the diamond drill bit is a total drilling depth of 500 meters, then N = 500 / 5 = 100, meaning 100 historical acoustic emission signals are collected, where N is an integer greater than a first preset number (e.g., 80). Each time the diamond drill bit completes a preset drilling workload (i.e., every 5 meters drilled), the diamond drill bit is removed from the hole, and a wear test is performed. The working surface of the diamond drill bit is photographed and image analyzed using an industrial microscope to determine the percentage of diamond particle detachment area on the working surface, thus obtaining 100 detachment area percentages. For example, the percentage of detached area is 2% in the first test, 35% in the 50th test, and 92% in the 100th test. Based on these 100 percentages of detached area, 100 historical measured wear values ​​are determined, each corresponding to one of the 100 historical acoustic emission signals. Specifically, since the percentage of detached diamond particles directly reflects the wear level of the diamond drill bit's working surface, a larger percentage indicates more severe wear. Therefore, the percentage value of each detached area is directly used as the historical measured wear value at the corresponding moment. For example, if the detached area percentage is 2% in the first test, the corresponding historical measured wear value is 2%; if it is 35% in the 50th test, the corresponding historical measured wear value is 35%; and if it is 92% in the 100th test, the corresponding historical measured wear value is 92%. Thus, a one-to-one correspondence is established between the 100 percentages of detached area and the 100 historical acoustic emission signals, collectively constituting the 100 historical measured wear values.

[0047] In the above embodiment, the 100 historical measured wear values ​​are divided into wear stages. For example, based on the changing pattern of the wear rate, M=3 physical wear stages are obtained, namely the initial break-in stage (the proportion of detached area from 0% to 15%), the stable wear stage (the proportion of detached area from 15% to 70%), and the severe wear stage (the proportion of detached area from 70% to 100%). M is a positive integer greater than a second preset number (e.g., the second preset number is 2) and less than N. Spectral analysis is performed on the 100 historical acoustic emission signals to extract the energy distribution of 100 historical characteristic frequency bands corresponding one-to-one with the 100 historical measured wear values. For example, energy values ​​in the three frequency bands of 50 kHz to 100 kHz, 100 kHz to 200 kHz, and 200 kHz to 400 kHz are extracted respectively. Finally, a target wear stage feature model was established based on the energy distribution of 100 historical characteristic frequency bands and the three physical wear stages. This target wear stage feature model is used to describe the characteristic relationship between the historical measured wear amount and the historical characteristic frequency band energy distribution corresponding to each of the three physical wear stages.

[0048] In an optional embodiment, a target wear stage feature model is established based on M physical wear stages and N historical characteristic frequency band energy distributions. Specifically, this includes: extracting a set of scalarized feature parameters from the N historical characteristic frequency band energy distributions; weighting and combining the set of scalarized feature parameters to obtain a comprehensive feature index; establishing a scatter plot of the diamond drill bit's entire lifecycle with N historical measured wear amounts as the abscissa and the comprehensive feature index as the ordinate; performing nonlinear trend analysis on the entire lifecycle scatter plot to determine the inflection point of the wear rate change trend, where the projection value of the inflection point on the abscissa is the boundary between every two adjacent physical wear stages in the M physical wear stages; using the inflection point of the wear rate change trend to perform piecewise data fitting on the entire lifecycle scatter plot, generating M feature sub-curves corresponding one-to-one with the M physical wear stages; and smoothly connecting the M feature sub-curves to establish a piecewise target wear stage feature model.

[0049] A set of scalarized feature parameters refers to a series of quantified parameters extracted from the energy distribution of historical characteristic frequency bands that can effectively represent their core information, such as the average energy, peak frequency, energy entropy, and kurtosis of a specific frequency band. A comprehensive feature index refers to a quantified index formed by weighting and summing multiple extracted scalarized feature parameters according to their different sensitivities to wear states, or by combining them through more complex functions; it can more comprehensively reflect the comprehensive characteristics of the signal. A full life-cycle scatter plot is a two-dimensional coordinate graph where the X-axis represents N historical measured wear amounts, and the Y-axis represents N corresponding comprehensive feature indices. Each point on the graph represents the signal-wear correspondence of the drill bit under a certain wear state. Nonlinear trend analysis refers to using mathematical or statistical methods to analyze the overall trend and local rate of change of the scatter plot to reveal the nonlinear law of the comprehensive feature index changing with the wear amount. The inflection point of the wear rate change trend refers to the critical point on the trend curve of the scatter plot where the slope (i.e., the representation of the wear rate) changes significantly; these points usually correspond to the transition of the physical wear stage. The M characteristic sub-curves refer to the M independent curves obtained by dividing the entire life cycle scatter plot into M data segments using the inflection points of the wear rate change trend, and then independently fitting a function to each data segment. The segmented target wear stage characteristic model refers to the complete model that is ultimately composed of these M smoothly connected characteristic sub-curves, which can accurately describe the relationship between signal characteristics and wear amount under different wear stages.

[0050] In the above embodiment, the modeling process described above will continue to be used as an example. A set of standardized feature parameters is extracted from the energy distributions of 100 historical characteristic frequency bands. For example, three standardized feature parameters are extracted from each historical characteristic frequency band energy distribution: the average energy value E1 of the 50 kHz to 100 kHz band, the average energy value E2 of the 100 kHz to 200 kHz band, and the average energy value E3 of the 200 kHz to 400 kHz band. A weighted combination of the set of standardized feature parameters is performed to obtain a comprehensive feature index. Specifically, for each historical characteristic frequency band energy distribution, the three standardized feature parameters E1, E2, and E3 extracted from that historical characteristic frequency band energy distribution are weighted and combined. Based on the sensitivity of each standardized feature parameter to the wear state, the weights are set to 0.3, 0.5, and 0.2, respectively. Then, the comprehensive feature index CI corresponding to that historical characteristic frequency band energy distribution is 0.3×E1 + 0.5×E2 + 0.2×E3. Therefore, the energy distribution of 100 historical characteristic frequency bands corresponds to 100 comprehensive characteristic indicators. Using the 100 historical measured wear amounts as the x-axis and the corresponding 100 comprehensive characteristic indicators as the y-axis, a scatter plot of the entire life cycle of the diamond drill bit is established. This scatter plot contains 100 data points, each representing the correspondence between the historical measured wear amount and the comprehensive characteristic indicator of the diamond drill bit under a certain wear state. Nonlinear trend analysis is performed on the scatter plot (e.g., using local weighted regression or moving window slope analysis) to determine the inflection points of the wear rate change trend. For example, the analysis reveals that at historical measured wear amounts of 15% and 70%, the slope of the comprehensive characteristic indicator changes significantly with the wear amount, identifying two inflection points of the wear rate change trend. The projection values ​​of these two inflection points on the x-axis are 15% and 70%, respectively, which represent the boundaries between every two adjacent physical wear stages in the three physical wear stages. The entire lifecycle scatter plot is divided into three data segments using two inflection points in the wear rate change trend. For each segment, piecewise data fitting is performed (e.g., an exponential function is used for the initial break-in stage, a linear function for the stable wear stage, and a quadratic polynomial function for the severe wear stage), generating three characteristic sub-curves corresponding one-to-one with the three physical wear stages. These three characteristic sub-curves are then smoothly connected at the inflection points of the wear rate change trend (e.g., spline interpolation is used to ensure continuity and smoothness at the connection points) to establish a piecewise characteristic model of the target wear stage.

[0051] In an optional embodiment, the degree of drill bit wear of the down-the-hole drill rig is determined based on the drill bit wear signal, specifically including: extracting signal features from the drill bit wear signal to obtain the current characteristic frequency band energy distribution related to the diamond drill bit wear of the down-the-hole drill rig; and inputting the current characteristic frequency band energy distribution into the target wear stage feature model to determine the degree of drill bit wear of the diamond drill bit.

[0052] Among them, signal feature extraction refers to extracting information that reflects the signal characteristics from the drill bit wear signal; current characteristic frequency band energy distribution refers to the energy distribution of characteristic frequency bands related to diamond drill bit wear extracted from the drill bit wear signal; target wear stage feature model refers to the previously established model used to describe the relationship between historical measured wear amount and historical characteristic frequency band energy distribution in different physical wear stages; and the degree of diamond drill bit wear refers to the severity of diamond drill bit wear.

[0053] In the above embodiments, a specific online wear assessment process is used as an example for illustration. During the actual drilling process, the drill bit wear signal, determined after dual verification of speed and position, is input to the signal processing unit. Signal feature extraction is performed on this drill bit wear signal. Specifically, spectral analysis is conducted on the drill bit wear signal (e.g., using the same Fourier transform method as in the modeling stage), extracting the energy distribution within three frequency bands: 50 kHz to 100 kHz, 100 kHz to 200 kHz, and 200 kHz to 400 kHz. This yields the current characteristic frequency band energy distribution related to the wear of the diamond drill bit on the down-the-hole drilling rig. Then, the current characteristic frequency band energy distribution is input to the established target wear stage feature model. The target wear stage feature model matches and calculates the current characteristic frequency band energy distribution based on the feature relationships corresponding to the three physical wear stages stored internally, ultimately determining the degree of drill bit wear. For example, the drill bit wear level output by the target wear stage feature model is 45%, indicating that the area of ​​diamond particles falling off the working surface of the diamond drill bit accounts for about 45%, and it is currently in a stable wear stage.

[0054] In an optional embodiment, the current characteristic frequency band energy distribution is input into the target wear stage feature model to determine the wear degree of the diamond drill bit. Specifically, this includes: inputting the current characteristic frequency band energy distribution into the target wear stage feature model, causing the target wear stage feature model to perform the following operations: the target wear stage feature model performs scalar feature extraction on the current characteristic frequency band energy distribution to obtain the current comprehensive feature index; the target wear stage feature model performs wear stage matching on the current comprehensive feature index to determine the target feature sub-curve corresponding to the current comprehensive feature index; the target wear stage feature model performs back interpolation calculation on the current feature sub-curve based on the current comprehensive feature index to obtain the target historical measured wear amount corresponding to the current comprehensive feature index; and the target historical measured wear amount is determined as the wear degree of the drill bit.

[0055] Among them, scalarized feature extraction refers to extracting quantized parameters that can represent the core information from the energy distribution of the current characteristic frequency band; the current comprehensive feature index refers to the numerical index obtained by weighting and combining the current scalarized feature parameters; wear stage matching refers to comparing the current comprehensive feature index with the features of each physical wear stage to find the corresponding stage; the target feature sub-curve refers to the feature sub-curve corresponding to the current comprehensive feature index, that is, the feature sub-curve corresponding to the value range in which the current comprehensive feature index falls, determined from M feature sub-curves through wear stage matching. In the claims, "current feature sub-curve" refers to the target feature sub-curve; reverse interpolation calculation refers to calculating the corresponding historical measured wear amount on the target feature sub-curve based on the current comprehensive feature index. Specifically, the current comprehensive feature index is used as the vertical coordinate input value of the target feature sub-curve, and the horizontal coordinate value corresponding to the vertical coordinate value is obtained through interpolation. The horizontal coordinate value is the target historical measured wear amount; the target historical measured wear amount refers to the historical measured wear amount corresponding to the current comprehensive feature index obtained through reverse interpolation calculation.

[0056] In the above embodiments, the internal processing flow of the target wear stage feature model is described in detail using the online wear assessment process as an example. After the current characteristic frequency band energy distribution is input into the target wear stage feature model, the target wear stage feature model performs the following operations: First, the target wear stage feature model performs scalar feature extraction on the current characteristic frequency band energy distribution, using the same extraction method and weight coefficients as in the modeling stage (i.e., weights of 0.3, 0.5, and 0.2 respectively), to obtain the current comprehensive feature index. For example, if the three current scalar feature parameters E1'=0.45, E2'=0.72, and E3'=0.38 are extracted, then the current comprehensive feature index CI'=0.3×0.45+0.5×0.72+0.2×0.38=0.135+0.36+0.076=0.571. Then, the target wear stage feature model matches the current comprehensive feature index 0.571 to the wear stage, comparing the current comprehensive feature index 0.571 with the range of the ordinate values ​​of the three feature sub-curves. It determines that the current comprehensive feature index 0.571 falls within the range of the feature sub-curve corresponding to the stable wear stage, thus identifying the target feature sub-curve corresponding to the current comprehensive feature index as the feature sub-curve of the stable wear stage. Next, the target wear stage feature model performs reverse interpolation calculation on the current feature sub-curve (i.e., the feature sub-curve of the stable wear stage) based on the current comprehensive feature index 0.571. This involves finding the abscissa value corresponding to the ordinate value of 0.571 on the feature sub-curve, obtaining the target historical measured wear amount corresponding to the current comprehensive feature index as 45%. Finally, the target historical measured wear amount of 45% is determined as the wear degree of the drill bit, meaning the current wear degree of the diamond drill bit is 45%.

[0057] In an optional embodiment, the target axial spacing between at least three acoustic emission sensors is determined, and the actual propagation speed of the same acoustic emission signal along the drill pipe is determined based on the target axial spacing and at least three signal reception times. Specifically, this includes: labeling at least three acoustic emission sensors distributed from top to bottom along the drill pipe axis as a first sensor, a second sensor, and a third sensor, respectively; labeling the signal reception times as a first reception time, a second reception time, and a third reception time, respectively; determining a first reception time difference between the first and second reception times; and determining a second reception time difference between the second and third reception times; determining a first axial spacing between the first and second sensors; and determining the first axial spacing between the first and second sensors. The second axial distance between the second sensor and the third sensor, the target axial distance includes the first axial distance and the second axial distance; the first propagation speed of the same acoustic emission signal along the first axial distance is determined according to the first receiving time difference and the first axial distance; the second propagation speed of the same acoustic emission signal along the second axial distance is determined according to the second receiving time difference and the second axial distance; the first propagation speed and the second propagation speed are checked for deviation, and the deviation check result is obtained; when the absolute deviation value of the first propagation speed and the second propagation speed is less than the preset speed check threshold, the average speed of the first propagation speed and the second propagation speed is determined, and the average speed is determined as the actual propagation speed.

[0058] The first, second, and third sensors are designated as such for ease of description, representing the ordered labeling of three sensors arranged along the drill pipe axis from top to bottom (or bottom to top). The first reception time, second reception time, and third reception time refer to the arrival timestamps of the same acoustic emission signal recorded by each of these three sensors, corresponding to their respective labels. The first reception time difference is the absolute value of the difference between the time when the first sensor receives the same acoustic emission signal (first reception time) and the time when the second sensor receives the same acoustic emission signal (second reception time). It precisely represents the time taken for the same acoustic emission signal to propagate between the first and second sensors. If the first, second, and third sensors are labeled S1, S2, and S3, respectively, and their reception times are t1, t2, and t3, then the first reception time difference Δt1 = |t1-t2|. The second reception time difference refers to the absolute value of the difference between the time when the second sensor receives the same acoustic emission signal (second reception time) and the time when the third sensor receives the same acoustic emission signal (third reception time). It precisely represents the time taken for the same acoustic emission signal to propagate between the second and third sensors. Using the above notation, the second reception time difference Δt2 = |t2 - t3|.

[0059] The first axial spacing refers to the precise physical distance measured along the drill pipe axis between the first and second sensors mounted on the drill pipe. This distance is a known parameter that is pre-measured and fixed during sensor installation. If the sensor positions are marked as L1, L2, and L3, then the first axial spacing d1 = |L2 - L1|. The second axial spacing refers to the precise physical distance measured along the drill pipe axis between the second and third sensors mounted on the drill pipe. Similarly, this is also a pre-measured and fixed known parameter. Using the above markings, the second axial spacing d2 = |L3 - L2|. The first propagation velocity refers to the propagation velocity of the acoustic emission signal in the first interval (i.e., between the first and second sensors), calculated using the first reception time difference (Δt1) and the first axial spacing (d1). Its calculation formula is v1 = d1 / Δt1. This velocity value reflects the signal propagation characteristics in this specific segment. The second propagation velocity refers to the propagation speed of the acoustic emission signal in the second segment (i.e., between the second and third sensors), independently calculated using the second reception time difference (Δt2) and the second axial distance (d2). Its calculation formula is v2 = d2 / Δt2. This velocity value reflects the signal propagation characteristics in the next adjacent segment. Deviation verification is the process of comparing these two independently calculated propagation velocities to check their consistency. The absolute deviation value is the absolute value of the difference between the first and second propagation velocities, i.e., |v1 - v2|.

[0060] The preset speed verification threshold is a very small tolerance limit set for this absolute deviation value. If the absolute deviation value exceeds the preset speed verification threshold, the measurement is considered unreliable. The setting of the preset speed verification threshold is closely related to the characteristics of the drill pipe material. Acoustic emission signals propagate along the drill pipe, and the drill pipe material can affect the basic characteristics of signal propagation, such as the speed of sound. Drill pipes made of different materials have differences in their internal microstructure, density, elastic modulus, etc., and these factors will significantly affect the propagation speed and stability of the acoustic emission signal. For example, drill pipes made of metal materials have relatively stable acoustic emission signal propagation due to their good rigidity and continuity; while the signal propagation characteristics of some composite material drill pipes may be affected by the material composition distribution, bonding method, etc. When the signal propagates in different sections of the drill pipe, if the drill pipe material is uniform, the first propagation speed and the second propagation speed should be similar. Material characteristics affect the speed of sound, and thus affect the preset speed verification threshold. For example, setting 1% or 2% of the material's speed of sound as the threshold is to comprehensively consider the influence of the drill pipe material on the stability of signal propagation, so as to ensure that the verification results accurately determine the signal propagation state and eliminate abnormal signal interference caused by the characteristics of the drill pipe material.

[0061] In the above embodiment, a specific propagation speed calculation and verification process is used as an example. Assume that three acoustic emission sensors are installed axially from top to bottom on the drill rod of a down-the-hole drill rig, labeled as sensor 1 (1 meter from the top of the drill rod), sensor 2 (5 meters from the top of the drill rod), and sensor 3 (9 meters from the top of the drill rod). The same acoustic emission signal propagates upwards along the drill rod from the working area of ​​the drill bit. Therefore, sensor 3 receives the same acoustic emission signal first, and sensor 1 receives it last. The corresponding signal reception times are labeled as: first reception time t1 = 10.001600 seconds, second reception time t2 = 10.000820 seconds, and third reception time t3 = 10.000000 seconds. The first reception time difference Δt1 is determined as |t2-t1|=|10.000820-10.001600|=0.000780 seconds (i.e., the time taken for the same acoustic emission signal to propagate from the second sensor position to the first sensor position), and the second reception time difference Δt2 is determined as |t3-t2|=|10.000000-10.000820|=0.000820 seconds (i.e., the time taken for the same acoustic emission signal to propagate from the third sensor position to the second sensor position).

[0062] In the above embodiment, the first axial distance d1 between the first sensor and the second sensor is determined to be 4 meters, and the second axial distance d2 between the second sensor and the third sensor is determined to be 4 meters. The target axial distance includes the first axial distance of 4 meters and the second axial distance of 4 meters. Based on the first reception time difference and the first axial distance, the first propagation speed of the same acoustic emission signal along the first axial distance is determined to be v1 = d1 / Δt1 = 4 / 0.000780 ≈ 5128 m / s. Based on the second reception time difference and the second axial distance, the second propagation speed of the same acoustic emission signal along the second axial distance is determined to be v2 = d2 / Δt2 = 4 / 0.000820 ≈ 4878 m / s. A deviation check is performed on the first and second propagation speeds to obtain the deviation check result. Specifically, the absolute deviation value |v1-v2| = |5128-4878| = 250 m / s is calculated. Assuming the preset speed verification threshold is set to 300 m / s (for example, based on approximately 5% of the sound velocity of alloy steel drill pipe material, which is about 5000 m / s), since the absolute deviation value of 250 m / s is less than the preset speed verification threshold of 300 m / s, the deviation between the first and second propagation velocities is determined to be within an acceptable range based on the deviation verification results. The average speed of the first and second propagation velocities is determined to be v = (v1 + v2) / 2 = (5128 + 4878) / 2 = 5003 m / s, and this average speed of 5003 m / s is determined as the actual propagation speed.

[0063] In an optional embodiment, the method further includes: determining the same acoustic emission signal as an interference signal when the absolute deviation value is greater than or equal to a preset speed verification threshold; or determining the same acoustic emission signal as an interference signal when the absolute deviation value is less than the preset speed verification threshold, and the actual propagation speed is less than the preset speed threshold or the axial position of the sound source is not in the drill bit working area; and filtering out the interference signal.

[0064] Among them, interference signal refers to acoustic emission signal that interferes with drill bit wear monitoring; actual propagation speed refers to the propagation speed of acoustic emission signal along drill rod; axial position of sound source refers to the position of the source of acoustic emission signal along drill rod axis; and drill bit working area refers to the area where drill bit performs drilling operations.

[0065] In the above embodiments, two specific interference signal identification scenarios are used as examples for illustration. In the first scenario, assuming that the three acoustic emission sensors receive another acoustic emission signal, the calculated first propagation speed is 3500 m / s, the second propagation speed is 4800 m / s, and the absolute deviation value |3500 - 4800| = 1300 m / s. Since the absolute deviation value of 1300 m / s is greater than or equal to the preset speed verification threshold of 300 m / s, it indicates that the propagation characteristics of the acoustic emission signal on the two sections of the drill pipe are seriously inconsistent, which may be caused by multipath propagation or propagation through non-drill pipe media. Therefore, this same acoustic emission signal is identified as an interference signal. In the second scenario, assuming that the three acoustic emission sensors receive yet another acoustic emission signal, the calculated first propagation speed is 3100 m / s, the second propagation speed is 3200 m / s, and the absolute deviation value |3100 - 3200| = 100 m / s. The absolute deviation value of 100 m / s is less than the preset speed verification threshold of 300 m / s, and the deviation verification is passed. The average speed is (3100+3200) / 2 = 3150 m / s, meaning the actual propagation speed is 3150 m / s. However, since the actual propagation speed of 3150 m / s is less than the preset speed threshold of 4500 m / s, it indicates that the acoustic emission signal is not propagating along the drill pipe steel medium. Therefore, this same acoustic emission signal is identified as an interference signal. In the third scenario, assuming that the three acoustic emission sensors receive another acoustic emission signal, the calculated first propagation speed is 5050 m / s, and the second propagation speed is 4950 m / s. The absolute deviation value |5050-4950| = 100 m / s, which is less than the preset speed verification threshold of 300 m / s, thus passing the deviation verification. The average speed is (5050+4950) / 2 = 5000 m / s, meaning the actual propagation speed is 5000 m / s, which is greater than the preset speed threshold of 4500 m / s, meeting the speed requirement. However, based on the reception time and actual propagation speed of the three signals, the axial position of the sound source was calculated to be 3 meters from the top of the drill pipe, outside the drill bit's working area (10 to 12 meters). This acoustic emission signal was likely caused by mechanical impact in the middle section of the drill pipe or stress redistribution in the rock mass. Therefore, this same acoustic emission signal was identified as an interference signal. The identified interference signal was filtered out and not included in the subsequent drill bit wear assessment.

[0066] Through the embodiments of this application, at least three acoustic emission sensors are distributed axially at intervals on the drill rod of a down-the-hole drill rig. Based on the target axial spacing and at least three signal reception times, the actual propagation speed of the same acoustic emission signal along the drill rod and the axial position of the sound source are simultaneously determined. A dual physical dimension screening criterion of velocity plus position is constructed. Combined with a triple verification interference filtering mechanism of deviation verification, velocity verification, and position verification, it is possible to fundamentally distinguish between valid drill bit wear signals and various complex interference signals. At the same time, through a pre-established target wear stage characteristic model, the frequency domain characteristics of the drill bit wear signal are accurately mapped into a quantified degree of drill bit wear, realizing real-time, accurate, and quantitative monitoring of the wear state of diamond drill bits on down-the-hole drill rigs.

[0067] The electronic device in the embodiments of this invention is described below from the perspective of hardware processing. (See attached document.) Figure 2 , Figure 2 This is a schematic diagram of the physical device structure of an electronic device in an embodiment of this application.

[0068] It should be noted that, Figure 2 The structure of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0069] like Figure 2 As shown, the electronic device includes a Central Processing Unit (CPU) 201, which can perform various appropriate actions and processes according to a program stored in Read-Only Memory (ROM) 202 or a program loaded from storage portion 208 into Random Access Memory (RAM) 203, such as performing the methods described in the above embodiments. The RAM 203 also stores various programs and data required for system operation. The CPU 201, ROM 202, and RAM 203 are interconnected via a bus 204. An Input / Output (I / O) interface 205 is also connected to the bus 204.

[0070] The following components are connected to I / O interface 205: input section 206 including audio input devices, push-button switches, etc.; output section 207 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 208 including a hard disk, etc.; and communication section 209 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 209 performs communication processing via a network such as the Internet. Drive 210 is also connected to I / O interface 205 as needed. Removable media 211, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 210 as needed so that computer programs read from them can be installed into storage section 208 as needed.

[0071] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 209, and / or installed from removable medium 211. When the computer program is executed by central processing unit (CPU) 201, it performs the various functions defined in the present invention.

[0072] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0073] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0074] Specifically, the electronic device in this embodiment includes a processor and a memory. The memory stores a computer program, and when the computer program is executed by the processor, it implements the down-the-hole drill bit wear monitoring method provided in the above embodiment.

[0075] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The storage medium carries one or more computer programs that, when executed by a processor of the electronic device, cause the electronic device to implement the down-the-hole drill bit wear monitoring method provided in the above embodiments.

[0076] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0077] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for monitoring wear of down-the-hole drill bits, characterized in that, include: The same acoustic emission signal received by at least three acoustic emission sensors, which are installed on the drill rod of a down-the-hole drill and are spaced apart along the axial direction, is collected during the drilling process, and the reception times of at least three signals received by the at least three acoustic emission sensors are recorded. Determine the target axial spacing between the at least three acoustic emission sensors, and determine the actual propagation speed and sound source axial position of the same acoustic emission signal along the drill pipe based on the target axial spacing and the at least three signal reception times; When the actual propagation speed is determined to be greater than or equal to a preset speed threshold, and the axial position of the sound source is located in the working area of ​​the drill bit of the down-the-hole drill, the same acoustic emission signal is determined to be a drill bit wear signal propagating along the drill rod. The degree of drill bit wear of the down-the-hole drill is determined based on the drill bit wear signal.

2. The method according to claim 1, characterized in that, Before acquiring the same acoustic emission signal received by at least three acoustic emission sensors spaced axially on the drill rod of a down-the-hole drill during drilling, and recording at least three signal reception times of the same acoustic emission signal received by the at least three acoustic emission sensors, the method further includes: Under the target rock strata conditions, N historical acoustic emission signals of the diamond drill bit of the down-the-hole drill rig are collected within a complete life cycle according to the preset drilling workload, where N is an integer greater than a first preset number. Each time the diamond drill bit completes the preset drilling workload, a wear test is performed on the diamond drill bit to determine the percentage of the area where diamond particles fall off on the working surface of the diamond drill bit, so as to obtain N percentages of the area where the particles fall off. Based on the percentage of the N detachment areas, determine the N historical measured wear amounts that correspond one-to-one with the N historical acoustic emission signals; The N historical measured wear values ​​are divided into wear stages to obtain M physical wear stages; Extract N historical characteristic frequency band energy distributions that correspond one-to-one with the N historical measured wear amounts from the N historical acoustic emission signals, where M is a positive integer greater than a second preset number and less than N; A target wear stage feature model is established based on the M physical wear stages and the N historical characteristic frequency band energy distributions. The target wear stage feature model is used to describe the characteristic relationship between the historical measured wear amount and the historical characteristic frequency band energy distribution corresponding to each of the M physical wear stages.

3. The method according to claim 2, characterized in that, The establishment of the target wear stage feature model based on the M physical wear stages and the N historical characteristic frequency band energy distributions specifically includes: Extract a set of scalarized feature parameters from the energy distribution of N historical characteristic frequency bands; The scalarized feature parameters are weighted and combined to obtain a comprehensive feature index; Using the N historical measured wear values ​​as the horizontal axis and the comprehensive characteristic index as the vertical axis, a scatter plot of the entire life cycle of the diamond drill bit is established. Nonlinear trend analysis is performed on the scatter plot of the entire life cycle to determine the inflection point of the wear rate change trend. The projection value of the inflection point of the wear rate change trend on the horizontal axis is the boundary between every two adjacent physical wear stages in the M physical wear stages. The inflection point of the wear rate change trend is used to perform segmented data fitting on the full life cycle scatter plot to generate M feature sub-curves that correspond one-to-one with the M physical wear stages. The M feature sub-curves are smoothly connected to establish a segmented feature model of the target wear stage.

4. The method according to claim 1, characterized in that, The step of determining the degree of drill bit wear of the down-the-hole drill rig based on the drill bit wear signal specifically includes: Signal features are extracted from the drill bit wear signal to obtain the current characteristic frequency band energy distribution related to the wear of the diamond drill bit of the down-the-hole drilling rig; The current characteristic frequency band energy distribution is input into the target wear stage characteristic model to determine the degree of wear of the diamond drill bit.

5. The method according to claim 4, characterized in that, The step of inputting the current characteristic frequency band energy distribution into the target wear stage characteristic model to determine the wear degree of the diamond drill bit specifically includes: The current characteristic frequency band energy distribution is input into the target wear stage characteristic model, so that the target wear stage characteristic model performs the following operations: The target wear stage feature model performs scalar feature extraction on the energy distribution of the current feature frequency band to obtain the current comprehensive feature index; The target wear stage feature model performs wear stage matching on the current comprehensive feature index to determine the target feature sub-curve corresponding to the current comprehensive feature index; The target wear stage feature model performs reverse interpolation calculation on the current feature sub-curve based on the current comprehensive feature index to obtain the target historical measured wear amount corresponding to the current comprehensive feature index. The historical measured wear amount of the target is determined as the wear degree of the drill bit.

6. The method according to claim 1, characterized in that, The determination of the target axial distance between the at least three acoustic emission sensors, and the determination of the actual propagation speed of the same acoustic emission signal along the drill pipe based on the target axial distance and the reception time of the at least three signals, specifically includes: At least three acoustic emission sensors distributed from top to bottom along the drill pipe axis are respectively labeled as the first sensor, the second sensor, and the third sensor, and the signal reception times are respectively labeled as the first reception time, the second reception time, and the third reception time; Determine a first reception time difference between the first reception time and the second reception time, and determine a second reception time difference between the second reception time and the third reception time; A first axial distance between the first sensor and the second sensor is determined, and a second axial distance between the second sensor and the third sensor is determined, wherein the target axial distance includes the first axial distance and the second axial distance; The first propagation speed of the same acoustic emission signal along the first axial spacing is determined based on the first reception time difference and the first axial spacing. The second propagation speed of the same acoustic emission signal propagating along the second axial spacing is determined based on the second reception time difference and the second axial spacing. The deviation between the first propagation speed and the second propagation speed is checked to obtain the deviation check result; When the absolute deviation between the first propagation speed and the second propagation speed is determined to be less than a preset speed verification threshold based on the deviation verification result, the average speed of the first propagation speed and the second propagation speed is determined, and the average speed is determined as the actual propagation speed.

7. The method according to claim 6, characterized in that, The method further includes: When the absolute deviation value is determined to be greater than or equal to a preset speed verification threshold, the same acoustic emission signal is identified as an interference signal; or, When the absolute deviation value is less than the preset speed verification threshold, and the actual propagation speed is less than the preset speed threshold or the axial position of the sound source is not in the working area of ​​the drill bit, the same acoustic emission signal is identified as the interference signal. The interference signal is filtered out.

8. An electronic device, characterized in that, The electronic device includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the electronic device to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on an electronic device, the electronic device causes the electronic device to perform the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on an electronic device, it causes the electronic device to perform the method as described in any one of claims 1-7.

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