Health management method and device for coal mine underground crossheading crusher breaking shaft group

Abstract

The application provides a health management method and device for a crusher breaking shaft group of a coal mine underground crossheading, and relates to the technical field of intelligent monitoring of coal mine machinery. The method comprises the following steps: inputting the vibration acceleration, temperature, motor input torque and grease metal chip content of a bearing into a bearing life benchmark model established through an orthogonal test to obtain benchmark life data; using the gangue content of a working face to construct a correction coefficient, correcting the benchmark life data to obtain dynamic life data; and determining the damage accumulation and average damage rate according to the dynamic life data based on the cumulative damage theory, and then determining the residual life of the bearing. The application fuses multiple-dimensional monitoring parameters to establish a dynamic life model, comprehensively reflects the coupling influence of multiple-dimensional data on the life of the bearing, quantitatively converts real-time monitoring data into residual life prediction results, accurately matches the maintenance decision with the actual health state of the bearing, and effectively avoids over-repair waste and under-repair shutdown risks.

Description

Technical Field

[0001] This application relates to the field of intelligent monitoring technology for coal mining machinery, and in particular to a health management method and device for the crusher shaft assembly of a crusher used in underground coal mine roadways. Background Technology

[0002] The crusher in the underground coal mine roadway is one of the key pieces of equipment in a fully mechanized mining face. It is mainly used to crush large pieces of coal and gangue transported by the scraper conveyor in the roadway. The crusher is usually located in the landing section of the conveyor, where the working environment is harsh and the load impact is large. As the core component of the crusher, the crushing shaft assembly mainly consists of crushing hammers (cutter teeth), a central shaft, cutter tooth seats, bearing seats, bearings, and a labyrinth, and rotates under strong impact loads through bearing support.

[0003] In actual operating conditions, the crusher shaft assembly is subjected to intense alternating loads and impacts, and its reliability directly affects the continuous and efficient mining of coal. The lifespan of the crusher shaft assembly is influenced by a combination of factors. Maintenance decisions in related technologies often rely on periodic inspections or reactive repairs, which can easily lead to resource waste from over-maintenance or safety hazards from under-maintenance. Traditional monitoring methods are limited to single threshold alarms for bearing temperature and cannot provide a comprehensive assessment from a lifespan perspective. Therefore, there is an urgent need for a monitoring method that can accurately control the health status of the crusher shaft assembly. Summary of the Invention

[0004] This application aims to at least partially address the technical problem of over-maintenance or under-maintenance of crusher shaft assemblies, which makes it impossible to accurately control the health status of the crusher shaft assemblies.

[0005] Therefore, the first aspect of this application proposes a health management method for the crusher shaft assembly of a crusher used in an underground coal mine roadway, the method comprising the following steps:

[0006] The vibration acceleration data of the bearing in the crushing shaft assembly, the temperature of the bearing, the motor input torque of the crushing shaft assembly, the grease and metal scrap content, and the gangue content of the working face are obtained. The vibration acceleration data, temperature, motor input torque, and grease / metal shavings content are input into the bearing life benchmark model established through orthogonal experiments to obtain the bearing benchmark life data. The bearing life benchmark model is a power function model obtained by performing multivariate nonlinear regression fitting based on orthogonal experimental data, with vibration acceleration data, temperature, motor input torque, and grease / metal shavings content as variables. A gangue content correction coefficient is constructed using the gangue content of the working face, and the baseline life data of the bearing is corrected to obtain the dynamic life data of the bearing. Based on the cumulative damage theory, the cumulative damage amount and average damage rate of the bearing are determined according to the dynamic life data. The remaining life data of the bearing is determined based on the cumulative damage and the average damage rate.

[0007] In some embodiments of this application, the step of constructing a gangue content correction coefficient using the gangue content of the working face to correct the reference life data of the bearing and obtain the dynamic life data of the bearing includes: using the ratio of the reference life data of the bearing to the gangue content correction coefficient as the dynamic life data of the bearing.

[0008] In some embodiments of this application, the bearing life reference model is represented as follows:

[0009] in, This refers to the baseline life data of the bearing. The input torque is given to the motor. The temperature is [temperature value]. The vibration acceleration data, The content of the grease and metal scraps, , , , , is a coefficient.

[0010] In some embodiments of this application, the gangue content correction factor is expressed as follows:

[0011] in, The content of gangue in the working face. This is the correction factor for the gangue content. , is a coefficient.

[0012] In some embodiments of this application, based on the cumulative damage theory, the cumulative damage of the bearing is determined using the following formula according to the dynamic life data:

[0013] in, This represents the cumulative damage to the bearing. For the first The number of bearing revolutions within a time step For the first Dynamic lifetime data within each time step. m represents the total number of time steps up to the current moment.

[0014] In some embodiments of this application, the remaining life data of the bearing is determined based on the accumulated damage and the average damage rate using the following formula:

[0015] in, This refers to the remaining life data of the bearing. This represents the cumulative damage to the bearing. This represents the average damage rate.

[0016] In some embodiments of this application, the method further includes: performing machine lifespan-based graded alarms based on the remaining lifespan data and preset alarm thresholds.

[0017] In some embodiments of this application, the method further includes: acquiring images of the hammer surface in the crusher shaft assembly using an explosion-proof industrial camera at the crusher's shutdown window; determining the current residual thickness of the hammer wear-resistant layer based on the hammer surface image; calculating the wear percentage by combining the current residual thickness with the preset original thickness of the wear-resistant layer; and performing wear classification and early warning based on the wear percentage.

[0018] A second aspect of this application provides a health management device for the crusher shaft assembly of a crusher used in an underground coal mine roadway, the device comprising: The acquisition module is used to acquire the vibration acceleration data of the bearing in the crushing shaft assembly, the temperature of the bearing, the motor input torque of the crushing shaft assembly, the content of grease and metal scraps, and the content of gangue on the working face; The first determining module is used to input the vibration acceleration data, the temperature, the motor input torque, and the grease and metal shavings content into the bearing life benchmark model established through orthogonal experiments to obtain the benchmark life data of the bearing. The bearing life benchmark model is a power function model obtained by performing multivariate nonlinear regression fitting based on orthogonal experimental data, with vibration acceleration data, temperature, motor input torque, and grease and metal shavings content as variables. The second determining module is used to construct a gangue content correction coefficient using the gangue content of the working face, correct the reference life data of the bearing, and obtain the dynamic life data of the bearing. The third determining module is used to determine the cumulative damage amount and average damage rate of the bearing based on the cumulative damage theory and the dynamic life data. The fourth determining module is used to determine the remaining life data of the bearing based on the accumulated damage amount and the average damage rate.

[0019] A third aspect of this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method described in the first aspect above.

[0020] The health management method for crusher shaft assemblies used in underground coal mine roadways provided in this application collects multi-dimensional parameters in real time, including vibration acceleration, temperature, motor input torque, grease and metal scrap content, and gangue content at the working face. Based on a power function benchmark model established by orthogonal experiments, a gangue content correction coefficient is introduced to dynamically correct the benchmark life. This method comprehensively reflects the combined impact of load, temperature, vibration, lubrication status, and material changes on bearing life under complex working conditions, overcoming the shortcomings of traditional single-parameter threshold alarms that cannot accurately assess the actual deterioration state of bearings. Furthermore, based on cumulative damage theory, the dynamic life data is quantitatively converted into cumulative damage and average damage rate, thereby accurately predicting the remaining life. This ensures that maintenance timing precisely matches the actual health state of the bearing, effectively avoiding waste of spare parts due to over-maintenance or unplanned downtime due to insufficient maintenance, thus guaranteeing the safety and economy of continuous production in underground coal mines.

[0021] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0022] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A schematic flowchart illustrating a health management method for a crusher shaft assembly in an underground coal mine roadway, provided as an embodiment of this application; Figure 2 This is a schematic diagram of a lubrication pipeline and sensor arrangement for a crusher shaft assembly provided in an embodiment of this application, where 62 is the crusher hammer and 621 is the wear-resistant layer. Figure 3 This is a schematic diagram of a health management device for a crusher shaft assembly in an underground coal mine roadway, provided as an embodiment of this application. Detailed Implementation

[0023] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0024] Specifically, the following describes a health management method and apparatus for the crusher shaft assembly of a crusher used in an underground coal mine roadway, with reference to the accompanying drawings.

[0025] Figure 1This is a flowchart illustrating a health management method for a crusher shaft assembly in an underground coal mine roadway, as provided in an embodiment of this application. The crusher shaft assembly includes bearings. Figure 1 As shown, the health management method for the crusher shaft assembly of the underground coal mine roadway crusher may include the following steps: Step 101: Obtain the vibration acceleration data of the bearing, the temperature of the bearing, the motor input torque of the crusher shaft assembly, the content of grease and metal scrap, and the content of gangue on the working face.

[0026] In some embodiments of this application, the motor input torque of the crusher shaft assembly can be calculated based on the motor torque and the reducer speed ratio, and used as a load characteristic parameter:

[0027] in, For the input torque of the motor, This is the motor torque. For transmission efficiency, This refers to the speed ratio of the reducer.

[0028] Step 102: Input the vibration acceleration data, temperature, motor input torque, and grease / metal shavings content into the bearing life benchmark model established through orthogonal experiments to obtain the bearing benchmark life data.

[0029] The bearing life benchmark model is a power function model obtained by performing multivariate nonlinear regression fitting based on orthogonal test data, using vibration acceleration data, temperature, motor input torque, and grease / metal scrap content as variables.

[0030] In some embodiments of this application, vibration acceleration data, temperature, motor input torque, and grease / metal shavings content can be used as influencing factors, with multiple levels set for each factor to cover a range of operating conditions from normal to severe. An orthogonal array is used to arrange multiple industrial tests (e.g., setting 3 levels and arranging 9 industrial tests). Under actual underground coal mine production conditions, each test operates the bearing under constant working conditions until failure, and the bearing life L (in revolutions or hours) of each test is recorded. Industrial tests can realistically reflect the impact of the complex underground environment on bearing life, including factors that are difficult to fully simulate in the laboratory, such as dust, humidity, and impact loads, which is more in line with engineering practice.

[0031] Multivariate nonlinear regression analysis was performed on the experimental data. Based on the physical characteristics of bearing life, the bearing life baseline model was assumed to be in power function form:

[0032] in, This is the baseline life data for the bearing. For the input torque of the motor, For temperature, For vibration acceleration data, The content of grease and metal scraps. , , , , is a coefficient.

[0033] Taking the logarithm of both sides of the equation, we can transform it into a linear form:

[0034] The coefficients of the bearing life benchmark model were solved by regression fitting of multiple sets of experimental data using the least squares method. The typical coefficient values ​​are obtained, and after substituting them, the bearing life reference model is obtained.

[0035] The bearing life benchmark model reflects the degree of influence of various factors on bearing life. Experiments show that load (motor input torque) has the most significant impact on life (the absolute value of the exponent is the largest), followed by temperature. Vibration and oil metal content also have important effects, which is consistent with the bearing failure mechanism.

[0036] Step 103: Construct a gangue content correction coefficient using the gangue content of the working face, correct the bearing's baseline life data, and obtain the bearing's dynamic life data.

[0037] In actual working conditions, the gangue content S at the working face directly affects the amplitude and frequency of the impact load, thereby accelerating bearing fatigue. In some embodiments of this application, a gangue content correction coefficient is obtained by fitting experimental data, as follows:

[0038] in, The content of gangue in the working face, This is the correction factor for gangue content. , is a coefficient.

[0039] In one implementation, the ratio of the bearing's baseline life data to the gangue content correction factor is used as the bearing's dynamic life data.

[0040] in, This refers to the dynamic life data of the bearing. This is the baseline life data for the bearing. This is the correction factor for gangue content. For the input torque of the motor, For temperature, For vibration acceleration data, The content of grease and metal scraps. , , , , , , is a coefficient.

[0041] Step 104: Based on the cumulative damage theory, determine the cumulative damage amount and average damage rate of the bearing according to the dynamic life data.

[0042] According to Miner's linear cumulative damage theory, the cumulative damage of the bearing under varying operating conditions is determined by the following formula:

[0043] in, This represents the cumulative damage to the bearing. For the first The number of bearing revolutions within a time step The first calculated based on fact parameters The dynamic lifetime data within each time step, where m is the total number of time steps up to the current moment. When the value reaches 1, the bearing fails.

[0044] The average damage rate d is the average damage rate under the current working conditions, which can be calculated using a sliding window:

[0045] Where k represents the number of time steps contained in the sliding window (for example, if we take the most recent 10 time steps, then k=10). For the first The dynamic life data of the bearing within n time steps, where n is the speed of the crusher shaft.

[0046] Step 105: Determine the remaining life data of the bearing based on the cumulative damage amount and the average damage rate.

[0047] In some embodiments of this application, the remaining life data of the bearing can be determined based on the cumulative damage amount and the average damage rate using the following formula:

[0048] in, This refers to the remaining life data of the bearing. This represents the cumulative damage to the bearing. This represents the average damage rate.

[0049] For example, the remaining life data of a bearing may include the percentage of remaining life and the predicted remaining time.

[0050] Optionally, in some embodiments of this application, machine lifespan-based graded alarms can be performed based on remaining lifespan data and preset alarm thresholds. Alarm information can be displayed on a host computer and simultaneously transmitted to a field display installed on the crusher housing, facilitating timely access by underground maintenance personnel. As an example, four alarm threshold levels can be set: Warning threshold: If the remaining lifespan is >20%, it is recommended to pay closer attention; optionally, if the remaining lifespan is >20%, and one of the following parameters—vibration acceleration data, temperature, motor input torque, grease and metal scrap content, and working face gangue content—rises slightly, it is also recommended to pay closer attention. Warning threshold: Remaining lifespan < 20%, indicating a planned shutdown for maintenance; Alarm threshold: If the remaining lifespan is less than 10%, an alarm will be triggered; optionally, an alarm may also be triggered when the parameter exceeds the limit value (such as a sharp rise in temperature). Shutdown threshold: If the remaining lifespan is less than 5%, a shutdown command will be output immediately to protect the equipment. Optionally, a shutdown command can also be output immediately when a certain parameter (such as a sudden vibration) reaches a dangerous value.

[0051] By implementing the embodiments of this application, multi-dimensional parameters such as vibration acceleration, temperature, motor input torque, grease and metal scrap content, and gangue content at the working face are collected in real time. Based on a power function benchmark model established by orthogonal experiments, a gangue content correction coefficient is introduced to dynamically correct the benchmark life. This comprehensively reflects the combined impact of load, temperature, vibration, lubrication status, and material changes on bearing life under complex working conditions, overcoming the shortcomings of traditional single-parameter threshold alarms that cannot accurately assess the actual deterioration state of bearings. Simultaneously, based on cumulative damage theory, dynamic life data is quantitatively converted into cumulative damage and average damage rate, thereby accurately predicting remaining life. This ensures that maintenance timing precisely matches the actual health state of the bearing, effectively avoiding waste of spare parts due to over-maintenance or unplanned downtime due to insufficient maintenance, thus guaranteeing the safety and economy of continuous production in underground coal mines.

[0052] In some embodiments of this application, the health monitoring of the crusher shaft assembly, in addition to monitoring the remaining life of the bearings, can also monitor the wear of the hammers in real time. The health management method for the crusher shaft assembly of a crusher used in underground coal mine roadways... Figure 1 Based on the illustrated embodiment, the following steps may also be included: S1, at the stop window of the crusher, an explosion-proof industrial camera captures images of the hammer surface.

[0053] During fixed windows of short shutdowns or maintenance breaks of the crusher, high-definition images of the crusher hammer are captured using a wireless explosion-proof industrial camera under auxiliary lighting, ensuring that the hammer is stationary and the image is clear without motion blur. The captured images can be transmitted to a host computer via a wireless network.

[0054] Optionally, the host computer can perform preprocessing such as filtering and denoising, grayscale conversion, and contrast enhancement on the acquired hammerhead surface image, and use an edge detection algorithm to extract the precise edge contour of the hammerhead, and perform preliminary matching and alignment with the pre-stored standard hammerhead contour.

[0055] S2, determine the current residual thickness of the hammerhead wear-resistant layer based on the hammerhead surface image.

[0056] In some embodiments of this application, binocular vision technology can be used to acquire three-dimensional point cloud data of the hammerhead surface and generate a depth map. By registering and comparing the acquired three-dimensional topography with a standard hammerhead three-dimensional model, the wear area is extracted, and the average depth of the wear area is calculated, thereby obtaining the current residual thickness of the wear-resistant layer.

[0057] S3 calculates the wear percentage by combining the current residual thickness and the preset original thickness of the wear-resistant layer.

[0058] The formula for calculating the percentage of wear can be found below:

[0059] in, The percentage of wear. The original thickness of the wear-resistant layer overlaid on the hammerhead. This represents the current residual thickness.

[0060] S4 provides wear classification and early warning based on wear percentage.

[0061] As an example, three wear level thresholds can be set and graded warnings can be issued: Mild wear: W < 30%, the system records wear data, displays a normal state, and does not trigger an alarm; Moderate wear: 30% ≤ W < 60%, the system issues a warning message, suggesting closer monitoring; Severe wear: W≥60%, the system issues a warning, indicating that the hammer head wear has reached a severe level, and recommends arranging for replacement as soon as possible.

[0062] When the current residual thickness When the wear layer thickness drops to its limit (which can be set to 20% of the original thickness, i.e., W=80%), the system issues an emergency alarm, requiring immediate shutdown and hammer replacement. This solves the problems of low efficiency and poor accuracy associated with traditional manual inspection, providing a scientific basis for hammer replacement. Simultaneously, the system predicts the remaining service life of the hammer based on historical wear rates and displays this prediction on the on-site monitor.

[0063] Figure 2 This is a schematic diagram of a crusher shaft assembly lubrication pipeline and sensor arrangement provided in an embodiment of this application. Figure 2As shown, the working surface of the crusher hammer 62 is overlaid with a wear-resistant layer 621, with an original thickness H0 of 10mm. During fixed windows of short shutdowns or maintenance intervals of the crusher, the system automatically triggers the industrial camera, auxiliary light source, and automatic lens cleaning device to acquire high-definition images of the hammer. Figure 3 As shown, the outermost curve is the theoretical profile of the hammerhead, the middle curve is the real-time acquired profile of the worn hammerhead, and the innermost curve is the profile of the hardened layer of the hammerhead. The wear percentage is calculated as W = H0 / (H0). The system calculates H) × 100% and provides tiered warnings based on preset thresholds. When H drops to 3mm (i.e., W=70%), the system issues a severe wear warning; when H drops to 2mm (W=80%), the system issues an emergency alarm, requiring immediate replacement, which is displayed on the field monitor and simultaneously uploaded to the central control center.

[0064] In one implementation, an explosion-proof display can be installed on the crusher housing to display the following content in real time: Bearing remaining life data; Current residual thickness and wear percentage of the hammerhead's wear-resistant layer; Vibration acceleration data, bearing temperature, motor input torque of the crusher shaft assembly, grease and metal shavings content, and gangue content at the working face; System alarm status and recommended maintenance measures; Historical data trend chart.

[0065] Downhole maintenance personnel can switch between display interfaces via touchscreen or buttons to view detailed data. The display has an audible and visual alarm function, automatically issuing a warning when an alarm threshold is triggered.

[0066] In one implementation, the host computer can upload the following data to the coal mine control center server in real time via an underground industrial Ethernet network: Bearing remaining life data; Current residual thickness and wear percentage of the hammerhead's wear-resistant layer; Vibration acceleration data, bearing temperature, motor input torque of the crusher shaft assembly, grease and metal shavings content, and gangue content at the working face; Alarm and fault records (such as time, type, threshold, and suggested actions); Equipment operation statistics (such as cumulative operating time, historical trend data, maintenance records).

[0067] The coal mine centralized control center can be equipped with an equipment health management platform, which monitors the operating status of all crushers and other key equipment in the mine in real time through a large-screen display system. When any equipment triggers an alarm, the alarm information automatically pops up on the large screen of the control center, and dispatchers are alerted through audible and visual alarms. The control center server stores and analyzes the uploaded data, generating equipment health reports and maintenance suggestions (such as predicting maintenance cycles and optimizing spare parts inventory), realizing centralized monitoring of all equipment in the mine, forming a two-level management system of field and centralized control, significantly improving operation and maintenance management efficiency, and providing data support for equipment maintenance decisions throughout the mine.

[0068] This application also proposes a health management system for the crusher shaft assembly of a crusher used in underground coal mine roadways. The health management system for the crusher shaft assembly of a crusher used in underground coal mine roadways may include a data acquisition layer, a data processing and modeling layer, and a health assessment and early warning layer.

[0069] The data acquisition layer adopts a wireless sensor network, including a bearing condition monitoring unit, a load monitoring unit, a lubrication monitoring unit, a material characteristic monitoring unit, and a hammer wear visual monitoring unit; an explosion-proof display is installed on the crusher housing to display the health status of the crusher shaft assembly in real time; the system uploads data to the coal mine control center via underground industrial Ethernet to achieve remote centralized monitoring.

[0070] The bearing condition monitoring unit can use a wireless temperature and vibration sensor, which integrates a vibration sensor and a temperature sensor. It is installed on the bearing housing of the crusher shaft assembly to collect the vibration acceleration data and temperature of the bearing in real time and transmit the data wirelessly. The load monitoring unit uses a wireless torque sensor, which is installed on the end of the crusher motor shaft, or reads the motor output torque from the frequency converter wirelessly. Combined with the speed ratio parameters of the reducer, it is used to calculate the motor input torque of the crusher shaft assembly. The lubrication monitoring unit uses a wireless oil quality sensor, which is installed on the grease outlet pipe of the lubrication system to monitor the metal shavings content of the grease, as well as parameters such as moisture and viscosity of the grease, and transmits the data wirelessly. The material characteristic monitoring unit obtains the gangue content (expressed as a percentage) of the working face through wireless communication. This information can be obtained through the coal mining machine's memory of cutting data, geological exploration data, or the added coal and rock identification sensor, and serves as a preliminary reference indicator reflecting the magnitude of the impact load. The hammerhead wear visual monitoring unit uses a wireless explosion-proof high-definition industrial camera, installed on the crusher housing cover, and equipped with an auxiliary light source. It is used to acquire images of the hammerhead surface during fixed windows of short shutdowns or maintenance intervals of the crusher. The working surface of the hammerhead is overlaid with a wear-resistant layer, the original thickness H0 of which is a known design value. The camera should be explosion-proof, waterproof, and dustproof, and can be equipped with an automatic lens cleaning device (such as water spray cleaning).

[0071] The wireless communication network includes wireless gateways or base stations deployed around the crusher to receive data from various wireless sensors and transmit it to a host computer.

[0072] The on-site display, installed on the side of the crusher housing or in an easily observable location, uses an explosion-proof LCD screen to display the remaining life data of the bearings, the percentage of hammer wear, the parameters of each sensor, and alarm information in real time, so that underground maintenance personnel can keep abreast of the health status of the crusher shaft assembly.

[0073] The remote data transmission and centralized control center connects the host computer to the coal mine's centralized control center server via an underground industrial Ethernet network. This allows the host computer to upload data such as the remaining lifespan of bearings, hammer wear percentage, real-time monitoring parameters, alarm information, and historical trend data to the centralized control center. The centralized control center is equipped with a large-screen display system that can monitor the real-time operating status of all crushers and other key equipment throughout the mine, and generate equipment health reports and maintenance recommendations. The data processing and modeling layer includes a wireless data receiving module, a programmable logic controller (PLC), and a host computer (or a downhole centralized control center server). The host computer has embedded bearing life prediction models and hammer wear analysis modules.

[0074] Figure 3 This is a schematic diagram of a health management device for the crusher shaft assembly of a crusher used in an underground coal mine roadway, provided as an embodiment of this application. Figure 3 As shown, the health management device for the crusher shaft assembly of the underground coal mine roadway crusher may include: an acquisition module 301, a first determination module 302, a second determination module 303, a third determination module 304, and a fourth determination module 305.

[0075] The acquisition module 301 is used to acquire the vibration acceleration data of the bearing, the temperature of the bearing, the motor input torque of the crusher shaft assembly, the content of grease and metal scrap, and the content of gangue on the working face; The first determining module 302 is used to input vibration acceleration data, temperature, motor input torque, and grease and metal shavings content into the bearing life benchmark model established through orthogonal experiments to obtain the bearing benchmark life data. The bearing life benchmark model is a power function model obtained by performing multivariate nonlinear regression fitting based on orthogonal experimental data, with vibration acceleration data, temperature, motor input torque, and grease and metal shavings content as variables. The second determining module 303 is used to construct a gangue content correction coefficient using the gangue content of the working face, correct the reference life data of the bearing, and obtain the dynamic life data of the bearing. The third determination module 304 is used to determine the cumulative damage amount and average damage rate of the bearing based on the cumulative damage theory and dynamic life data. The fourth determination module 305 is used to determine the remaining life data of the bearing based on the cumulative damage amount and the average damage rate.

[0076] In some embodiments of this application, such as Figure 3 Based on the illustrated embodiment, the health management device for the crusher shaft assembly of a crusher used in underground coal mine roadways may further include a hammer detection module. The hammer detection module is used to: acquire images of the hammer surface using an explosion-proof industrial camera at the crusher's shutdown window; determine the current residual thickness of the hammer wear-resistant layer based on the hammer surface images; calculate the wear percentage by combining the current residual thickness with the preset original thickness of the wear-resistant layer; and perform wear classification and early warning based on the wear percentage.

[0077] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0078] To implement the above embodiments, this application also proposes an electronic device, including: a processor and a memory communicatively connected to the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0079] To implement the above embodiments, this application also proposes a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods provided in the foregoing embodiments.

[0080] To implement the above embodiments, this application also proposes a computer program product, including a computer program that, when executed by a processor, implements the methods provided in the foregoing embodiments.

[0081] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0082] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0083] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0084] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0085] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0086] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0087] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0088] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for health management of the crusher shaft assembly of a crusher used in underground coal mine roadways, characterized in that, The method includes the following steps: The vibration acceleration data of the bearing in the crushing shaft assembly, the temperature of the bearing, the motor input torque of the crushing shaft assembly, the grease and metal scrap content, and the gangue content of the working face are obtained. The vibration acceleration data, temperature, motor input torque, and grease / metal shavings content are input into the bearing life benchmark model established through orthogonal experiments to obtain the bearing benchmark life data. The bearing life benchmark model is a power function model obtained by performing multivariate nonlinear regression fitting based on orthogonal experimental data, with vibration acceleration data, temperature, motor input torque, and grease / metal shavings content as variables. A gangue content correction coefficient is constructed using the gangue content of the working face, and the ratio of the bearing's baseline life data to the gangue content correction coefficient is used as the bearing's dynamic life data. Based on the cumulative damage theory, the cumulative damage amount and average damage rate of the bearing are determined according to the dynamic life data. The remaining life data of the bearing is determined based on the cumulative damage and the average damage rate; The bearing life benchmark model is represented as follows: This refers to the baseline life data of the bearing. The input torque is given to the motor. The temperature is... The vibration acceleration data, The content of the grease and metal scraps, , , , , For coefficients; The correction factor for gangue content is expressed as follows: The content of gangue in the working face. This is the correction factor for the gangue content. , is a coefficient.

2. The method according to claim 1, characterized in that, Based on the cumulative damage theory, the cumulative damage of the bearing is determined using the following formula based on the dynamic life data: in, This represents the cumulative damage to the bearing. For the first The number of bearing revolutions within a time step For the first Dynamic lifetime data within each time step. m represents the total number of time steps up to the current moment.

3. The method according to claim 2, characterized in that, The remaining life data of the bearing is determined based on the accumulated damage and the average damage rate using the following formula: in, This refers to the remaining life data of the bearing. This represents the cumulative damage to the bearing. This represents the average damage rate.

4. The method according to any one of claims 1-3, characterized in that, Also includes: Machine lifespan is graded and alarmed based on the remaining lifespan data and preset alarm thresholds.

5. The method according to claim 1, characterized in that, The method further includes: At the shutdown window of the crusher, an explosion-proof industrial camera is used to capture images of the hammer surface in the crusher shaft assembly. The current residual thickness of the hammerhead wear-resistant layer is determined based on the hammerhead surface image; The wear percentage is calculated by combining the current residual thickness and the preset original thickness of the wear-resistant layer; Wear classification and early warning are based on the wear percentage.

6. A health management device for the crusher shaft assembly of a crusher used in underground coal mine roadways, characterized in that, The device includes: The acquisition module is used to acquire the vibration acceleration data of the bearing in the crushing shaft assembly, the temperature of the bearing, the motor input torque of the crushing shaft assembly, the content of grease and metal scraps, and the content of gangue on the working face; The first determining module is used to input the vibration acceleration data, the temperature, the motor input torque, and the grease and metal shavings content into the bearing life benchmark model established through orthogonal experiments to obtain the benchmark life data of the bearing. The bearing life benchmark model is a power function model obtained by performing multivariate nonlinear regression fitting based on orthogonal experimental data, with vibration acceleration data, temperature, motor input torque, and grease and metal shavings content as variables. The second determining module is used to construct a gangue content correction coefficient using the gangue content of the working face, and to use the ratio of the bearing's baseline life data to the gangue content correction coefficient as the bearing's dynamic life data. The third determining module is used to determine the cumulative damage amount and average damage rate of the bearing based on the cumulative damage theory and the dynamic life data. The fourth determining module is used to determine the remaining life data of the bearing based on the accumulated damage amount and the average damage rate; The bearing life benchmark model is represented as follows: This refers to the baseline life data of the bearing. The input torque is given to the motor. The temperature is... The vibration acceleration data, The content of the grease and metal scraps, , , , , For coefficients; The correction factor for gangue content is expressed as follows: The content of gangue in the working face. This is the correction factor for the gangue content. , is a coefficient.

7. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-5.

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