A Smart Real-Time Crushing Status Assessment Method for Stone Crusher

Abstract

This invention discloses an intelligent method for real-time crushing status assessment of a crusher, specifically relating to the field of status assessment technology. The method includes installing laser particle size analyzers and flow sensors around the feed inlet and adjusting ring assembly to capture feed particle size and flow rate in real time; arranging acoustic sensors near the fixed cone assembly to record crushing sounds; utilizing the rigidity of the beamless machine body, securely installing vibration acceleration sensors on the main shaft bearing housing and frame; and placing speed and angle sensors at the eccentric steel sleeve to collect rotational characteristics. By analyzing the collected acoustic signals in the time and frequency domains, the acoustic characteristics of the crusher's operation are obtained. Based on multi-dimensional monitoring data analysis of the cone crusher's operating status, the operating condition characteristics of the crusher are obtained, and the real-time operating status of the crusher is assessed. This invention ensures that the equipment operates under optimal conditions, avoiding equipment failure and reducing maintenance costs.

Description

Technical Field

[0001] This invention relates to the field of condition assessment technology, and more specifically, to an intelligent method for real-time crushing condition assessment of a stone crusher. Background Technology

[0002] With the increasing demands for aggregate quality and production efficiency from industries such as mining, building materials, metallurgy, and power, cone crushers have become commonly used medium and fine crushing equipment. However, traditional methods for monitoring and maintaining the condition of crushers mainly rely on experience or periodic inspections. Most of these methods monitor load changes solely through motor current or hydraulic pressure, making it difficult to promptly detect signs of malfunctions such as internal blockages, overloads, or cavity wear. Furthermore, due to the complex internal structure and variable working environment of the equipment, cone crushers are susceptible to overloads, material jams, and uneven vibrations, leading to reduced production efficiency, increased equipment wear, and even shutdowns. Although sensor technology and data acquisition capabilities are constantly improving, many monitoring methods still rely on manual judgment and experience, lacking intelligent and automated decision support systems.

[0003] To address the aforementioned shortcomings, a technical solution is provided. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide an intelligent method for real-time crushing status evaluation of a stone crusher, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A smart method for real-time crushing status assessment of a stone crusher, specifically including the following steps:

[0007] S1: Install laser particle size analyzers and flow sensors around the feed inlet and adjusting ring assembly to capture feed particle size and flow rate in real time. Arrange acoustic sensors near the fixed cone assembly to record crushing sounds. Utilize the rigidity of the beamless machine body to securely install vibration acceleration sensors on the main shaft bearing housing and frame. Arrange speed and angle sensors at the eccentric steel sleeve to collect rotational characteristics.

[0008] S2: By analyzing the collected acoustic signals in the time and frequency domains, the acoustic characteristic information of the crusher's operation is obtained, and the working condition characteristic information of the crusher's operation is obtained based on the multi-dimensional monitoring data of the cone crusher's operating status.

[0009] S3: Comprehensively analyze the acoustic and operational characteristics of the crusher to evaluate its real-time operating status. The crusher's operating status is assessed by comparing preset thresholds.

[0010] In a preferred embodiment, the acoustic characteristic information and the operating condition characteristic information include:

[0011] Acoustic characteristic information is represented by the acoustic spectral entropy anomaly system, and operating condition characteristic information is represented by the crushing performance fluctuation coefficient and the cumulative coefficient of operational anomalies, where PS yc BD is the acoustic spectral entropy anomaly coefficient. ps YX represents the fluctuation coefficient of the fracture performance. jl This is the cumulative coefficient for operational anomalies.

[0012] In a preferred embodiment, the logic for obtaining the acoustic spectral entropy anomaly coefficient is as follows:

[0013] Acoustic sensors arranged at the fixed cone assembly are used to collect acoustic signals captured by the acoustic sensors during the operation of the cone crusher within the monitoring range. The acoustic signals are then converted from the time domain to the frequency domain using Fourier transform. The expression for Fourier transform is: X(f) = FFT(x(t)); where X(f) is the frequency domain signal and x(t) is the time domain signal.

[0014] The acoustic signal of the cone crusher during operation within the monitoring range is divided into low-frequency, mid-frequency, and high-frequency ranges. The power spectral density is calculated for each of the low-frequency, mid-frequency, and high-frequency ranges. The formula for calculating the power spectral density is as follows: Where H(P) is the power spectral density, P(f i () represents the frequency f i The normalized power spectral density value at the specified location, where N is the number of frequency components in the corresponding frequency range;

[0015] The standard coefficients for spectral entropy monitoring in different frequency ranges are calculated using the following formula: Among them, PS m H(P) represents the standard coefficient for spectral entropy monitoring in different frequency ranges. m Let m be the power spectral density of the m-th frequency interval. Let be the reference power spectral density under normal operating conditions of the device in the m-th frequency interval. This is the critical value at which the device malfunctions in the m-th frequency range;

[0016] The formula for calculating the acoustic spectral entropy anomaly coefficient is as follows: Where, α m These are the weighting coefficients for each frequency range.

[0017] In a preferred embodiment, the logic for obtaining the breakage performance fluctuation coefficient is as follows:

[0018] By installing a laser particle size analyzer, the particle size of the material at the feed inlet and discharge outlet is determined. The crushing ratio is obtained by comparing the particle size of the material at the feed inlet with that at the discharge outlet during the operation of the cone crusher within the monitoring range. The calculation formula is as follows: Among them, PS k This refers to the crushing ratio of the cone crusher during operation within the monitoring range. For the particle size of the material at the feed port, The particle size of the material at the discharge port is k = 1, 2, 3, ..., K, where K is a positive integer and k is the number of the particle size collected by the laser particle size analyzer within the monitoring interval.

[0019] Based on the crushing ratio of the cone crusher during operation within the monitoring range, calculate the standard deviation and average value of the crushing ratio within the monitoring range, and label the standard deviation and average value of the crushing ratio within the monitoring range as: STD ps and AVG ps ;in,

[0020] The formula for calculating the fluctuation coefficient of crushing performance is as follows:

[0021] In a preferred embodiment, the logic for obtaining the cumulative coefficient of operational anomalies is as follows:

[0022] Vibration acceleration, rotation speed, and angle signals of the cone crusher during operation within the monitoring range are obtained using vibration acceleration sensors, rotation speed sensors, and angle sensors. These signals are represented as JS(t), ZS(t), and JD(t). By setting threshold ranges for the vibration acceleration, rotation speed, and angle signals, the time periods during which the detected signals exceed the thresholds are obtained.

[0023] The cumulative coefficient for operational anomalies is calculated using the following formula: Among them, [t a ,t b [t] represents the time period during which the vibration acceleration signal is abnormal. c ,t d [t] represents the time period during which the speed signal is abnormal. e ,t f [This represents the time period during which the angle signal was abnormal.]

[0024] In a preferred embodiment, a comprehensive analysis is performed on the acoustic and operational characteristics of the crusher, including:

[0025] An operational evaluation model is constructed by weighting the acoustic spectral entropy anomaly coefficient, the breakage performance fluctuation coefficient, and the cumulative coefficient of operational anomalies, generating operational evaluation coefficients. The formula for calculating the operational evaluation coefficients is: YX pg =β1PS yc +β2BD ps +β3YX jl Among them, YX pg The coefficients are the operational evaluation coefficients, and β1, β2, and β3 are the proportional coefficients of the acoustic spectral entropy anomaly coefficient, the breakage performance fluctuation coefficient, and the operational anomaly cumulative coefficient, respectively. β1, β2, and β3 are all greater than 0.

[0026] In a preferred embodiment, the operating status of the crusher is evaluated by comparing preset thresholds, including:

[0027] Set an operating evaluation coefficient threshold. Compare the operating evaluation coefficient of the cone crusher with the operating evaluation coefficient threshold during operation within the monitoring range. If the operating evaluation coefficient is greater than the operating evaluation coefficient threshold, an early warning signal is generated. If the operating evaluation coefficient is less than the operating evaluation coefficient threshold, no early warning signal is generated.

[0028] The technical effects and advantages of this invention are as follows:

[0029] This invention utilizes various sensors, such as laser particle size analyzers, flow sensors, acoustic sensors, vibration acceleration sensors, speed sensors, and angle sensors, installed in key components like the feed inlet, adjusting ring assembly, fixed cone assembly, main shaft bearing housing, and eccentric steel sleeve. These sensors collect real-time information on material particle size, flow rate, vibration signals, rotational characteristics, and acoustic features. Through multi-dimensional monitoring and real-time data analysis, the operating status of the cone crusher is evaluated, ensuring that the equipment operates under optimal conditions, avoiding equipment failures, and reducing maintenance costs. Attached Figure Description

[0030] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0031] Figure 1 This is a flowchart illustrating an intelligent real-time crushing status assessment method for a stone crusher according to the present invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Example 1

[0034] Figure 1 This is a flowchart illustrating an intelligent real-time crushing status assessment method for a stone crusher according to the present invention, which specifically includes the following steps:

[0035] S1: Install laser particle size analyzers and flow sensors around the feed inlet and adjusting ring assembly to capture feed particle size and flow rate in real time. Arrange acoustic sensors near the fixed cone assembly to record crushing sounds. Utilize the rigidity of the beamless machine body to securely install vibration acceleration sensors on the main shaft bearing housing and frame. Arrange speed and angle sensors at the eccentric steel sleeve to collect rotational characteristics.

[0036] S2: By analyzing the collected acoustic signals in the time and frequency domains, the acoustic characteristic information of the crusher's operation is obtained, and the working condition characteristic information of the crusher's operation is obtained based on the multi-dimensional monitoring data of the cone crusher's operating status.

[0037] S3: Comprehensively analyze the acoustic and operational characteristics of the crusher to evaluate its real-time operating status. The crusher's operating status is assessed by comparing preset thresholds.

[0038] In mines, cone crushers are typically used to achieve the maximum crushing ratio and produce feed materials with the particle size required by downstream processes. A cone crusher consists of multiple components, including an adjusting ring assembly, a locking cylinder, an eccentric steel sleeve, a main frame, a beamless design, a feed port, a fixed cone assembly, a pinion, a horizontal shaft, a horizontal shaft copper sleeve, and a main shaft.

[0039] Specifically, the adjusting ring assembly is mainly used to adjust the size of the discharge opening of the cone crusher and control the output particle size. By adjusting the bolts or hydraulic system of the ring assembly, the size of the crushing chamber can be changed, thereby affecting the particle size of the crushed material. It is characterized by automatic adjustment through hydraulic means and is usually designed with high-strength steel to withstand the huge pressure generated during the crushing process.

[0040] The main function of the locking cylinder is to lock the connection between the fixed cone and the moving cone of the crusher, preventing loosening during the crushing process. It is used to fix adjusting rings or protective devices, ensuring stable operation of the crusher. It is typically a hydraulic system used to achieve precise positioning and locking of components within the crushing chamber, preventing loosening caused by equipment vibration.

[0041] The eccentric steel sleeve is part of the moving cone transmission system of a cone crusher, and it bears the eccentric motion of the moving cone. Its function is to cause the moving cone to generate eccentric motion, thereby pushing the material to be squeezed in the crushing chamber. A key feature is that the eccentric steel sleeve is usually made of wear-resistant material to ensure its durability and stability under high load operation.

[0042] The main frame is the basic structure of the cone crusher. It supports all the components of the crusher and withstands the enormous pressure generated during the crushing process. It is typically constructed from high-strength steel plates to ensure structural stability under high loads. The main frame is usually designed as a modular structure for easy maintenance and installation.

[0043] The beamless design refers to a crusher that lacks a traditional beam support structure inside, instead providing support and stability through a more compact and optimized design. This design helps reduce structural weight and improves crusher efficiency. Its key feature is ensuring stable operation and enhanced durability through rational stress distribution and structural design.

[0044] The feed inlet is the entrance for materials into the cone crusher, and its design is crucial to ensure smooth material flow into the crushing chamber. The smoothness of the feed inlet directly affects the crusher's feeding speed and crushing efficiency. A clear feed inlet reduces the likelihood of material blockage and prevents crusher malfunctions caused by material accumulation. Feed inlets are typically designed with an inclined angle to help materials flow quickly into the crushing chamber.

[0045] The fixed cone is a fixed conical component in a crusher that, together with the moving cone, forms the crushing chamber. The function of the fixed cone is to compress and crush the material entering the crushing chamber, working in conjunction with the moving cone to complete the crushing task. A key feature of the fixed cone is that its design requires wear resistance and high pressure resistance; it is typically made of high-strength materials such as alloy steel to ensure its long-term durability.

[0046] The pinion is an important component in the power transmission system of a crusher. It meshes with the large gear to help transmit the power of the main motor to the moving cone or other rotating parts, ensuring the normal operation of the crusher.

[0047] The horizontal shaft is a core component of a cone crusher, typically connecting the main motor and the moving cone. It transmits power from the motor to the moving cone, causing it to rotate and thus crush the material. It is usually made of high-strength steel, possessing sufficient strength and rigidity to withstand heavy loads.

[0048] The horizontal shaft copper bushing is used to support the horizontal shaft, reduce friction, and ensure smooth shaft rotation. It is a key lubrication component between the horizontal shaft and the frame, characterized by good thermal conductivity and wear resistance, effectively reducing friction and extending shaft service life.

[0049] The main shaft is one of the core components of a cone crusher. It connects the moving cone to the crusher's power system and bears the rotational motion of the moving cone. The rotation of the main shaft directly affects the crusher's efficiency and performance. It is typically made of high-strength alloy steel, possessing extremely high wear resistance and compressive strength, ensuring stability and durability during long-term use.

[0050] Laser particle size analyzers and flow sensors are installed around the feed inlet and adjusting ring assembly. The laser particle size analyzer is used to capture the particle size information of the material entering the crusher in real time. The laser particle size analyzer measures the particle size distribution of the material by emitting a laser beam. Real-time acquisition of the feed particle size helps to dynamically adjust the working state of the crusher and ensure that the equipment always operates under the best working conditions. The flow sensor is also installed at the feed inlet to monitor the feed flow rate in real time, that is, the rate at which the material enters the crusher. This helps to control the feed rate, avoid overload, and ensure the load balance of the crusher.

[0051] Vibration acceleration sensors are securely installed on the main shaft bearing housing and frame. During operation, the crusher generates vibration signals due to its complex mechanical forces. These vibration data can be collected in real time using vibration acceleration sensors, allowing identification of whether the equipment is operating normally or experiencing abnormal vibrations.

[0052] Rotation speed and angle sensors are installed at the eccentric steel sleeve to collect rotation characteristics. The eccentric steel sleeve is a key component that drives the rotation of the moving cone. Therefore, monitoring its rotation speed is very important. It can help determine the working status of the crusher and detect problems such as abnormal rotation speed in time. The angle sensor can understand the movement status of the crusher and avoid uneven material crushing or unnecessary equipment damage caused by abnormal angle.

[0053] Acoustic sensors are placed near the fixed cone assembly to record the crushing sound patterns. During operation, especially when materials are being crushed, the crusher will generate specific sound wave signals. The acoustic sensors can capture these sounds and analyze the characteristics of the acoustic signals to determine whether the crusher is working normally or whether any abnormalities have occurred.

[0054] Acoustic and operational characteristic information of the cone crusher during operation within the monitoring range is collected by monitoring equipment. The acoustic characteristic information is represented by the acoustic spectral entropy anomaly system, and the operational characteristic information is represented by the crushing performance fluctuation coefficient and the cumulative coefficient of operational anomaly.

[0055] The logic for obtaining the acoustic spectral entropy anomaly coefficient is as follows: based on the acoustic sensors arranged at the fixed cone assembly, the acoustic signals captured by the acoustic sensors during the operation of the cone crusher within the monitoring range are collected, and the acoustic signals are converted from the time domain to the frequency domain through Fourier transform. The expression of Fourier transform is: X(f)=FFT(x(t)); where X(f) is the frequency domain signal and x(t) is the time domain signal.

[0056] The acoustic signal of the cone crusher during operation within the monitoring range is divided into low-frequency, mid-frequency, and high-frequency ranges. The power spectral density is calculated for each of the low-frequency, mid-frequency, and high-frequency ranges. The formula for calculating the power spectral density is as follows: Where H(P) is the power spectral density, P(f i () represents the frequency f i The normalized power spectral density value at the specified location, where N is the number of frequency components in the corresponding frequency range;

[0057] It should be noted that the lower the power spectral density in different frequency ranges, the more concentrated the spectrum distribution of the cone crusher during operation within the monitoring range, and the stronger the regularity of the signal. This usually means that the cone crusher is working normally and operating stably. Conversely, it means that the equipment has abnormalities or malfunctions and the working state is unstable.

[0058] Based on the operating characteristics of equipment such as crushers, the frequency spectrum is typically divided into several typical frequency ranges. In the low-frequency range, signals are usually related to the basic vibration, structural vibration, and overall operating state of the machinery. For example, this includes the overall motion of the equipment, low-frequency impacts, and elastic deformation of moving parts. In the mid-frequency range, signals are typically related to the mid-frequency vibration of the equipment, the crushing of processed materials, and impact processes. Signals in this frequency band may be closely related to anomalies occurring during equipment operation (such as material jamming or pressure fluctuations). In the high-frequency range, signals are typically related to high-speed rotating parts, gear friction, and bearing condition. Signals in this frequency band may reflect wear, friction, or failure of internal components.

[0059] The standard coefficients for spectral entropy monitoring in different frequency ranges are calculated using the following formula: Among them, PS m H(P) represents the standard coefficient for spectral entropy monitoring in different frequency ranges. m Let m be the power spectral density of the m-th frequency interval. Let be the reference power spectral density under normal operating conditions of the device in the m-th frequency interval. This is the critical value at which the device malfunctions in the m-th frequency range;

[0060] The formula for calculating the acoustic spectral entropy anomaly coefficient is as follows: Among them, PS yc α is the acoustic spectral entropy anomaly coefficient. m These are the weighting coefficients for each frequency range.

[0061] As can be seen from the formula, the larger the acoustic spectral entropy anomaly coefficient, the higher the degree of abnormality in the equipment operation and the more unstable the working state. Conversely, it indicates that the operating signal of the equipment in each frequency range is relatively regular, the vibration signal is relatively stable, the equipment is operating well, and the working state is normal.

[0062] The logic for obtaining the crushing performance fluctuation coefficient is as follows: By installing a laser particle size analyzer, the particle size of the material at the feed inlet and discharge outlet is determined. The crushing ratio is obtained by comparing the particle size of the material at the feed inlet with the particle size of the material at the discharge outlet during the operation of the cone crusher within the monitoring range. The calculation formula is: Among them, PS k This refers to the crushing ratio of the cone crusher during operation within the monitoring range. For the particle size of the material at the feed port, The particle size of the material at the discharge port is k = 1, 2, 3, ..., K, where K is a positive integer and k is the number of the particle size collected by the laser particle size analyzer within the monitoring interval.

[0063] Based on the crushing ratio of the cone crusher during operation within the monitoring range, calculate the standard deviation and average value of the crushing ratio within the monitoring range, and label the standard deviation and average value of the crushing ratio within the monitoring range as: STD ps and AVG ps ;in,

[0064] The formula for calculating the fluctuation coefficient of crushing performance is as follows: Among them, BD ps The fluctuation coefficient represents the breakage behavior.

[0065] As the formula shows, a larger fluctuation coefficient in crushing performance indicates unstable equipment performance. The equipment may face problems such as uneven feeding, material jamming, and overload, leading to uneven crushing effects and lower working efficiency. Conversely, a balanced crushing effect and good working condition indicate optimal equipment performance. When the equipment operates under optimal conditions, the variation range of feed and discharge particle sizes is small, and the equipment's working efficiency is high.

[0066] The logic for obtaining the cumulative coefficient of the abnormal operation is as follows: by using a vibration acceleration sensor, a speed sensor, and an angle sensor, the vibration acceleration signal, speed signal, and angle signal of the cone crusher during operation within the monitoring range are obtained. The vibration acceleration signal, speed signal, and angle signal of the cone crusher during operation within the monitoring range are represented as JS(t), ZS(t), and JD(t). By setting the threshold range of the vibration acceleration signal, speed signal, and angle signal, the time period during which the detected signal exceeds the threshold is obtained.

[0067] It should be noted that if the acceleration value exceeds the predetermined threshold, it usually indicates that there are problems such as imbalance, overload, mechanical failure, or material jamming in the equipment. For example, bearing wear or gear damage will usually cause irregular vibration of the equipment, resulting in acceleration exceeding the normal range. If the angle of the moving cone exceeds the threshold, it may cause uneven movement of the equipment and unstable crushing effect. For example, if the eccentric steel sleeve, main shaft, or connecting parts are loose or damaged, it may cause excessive angle changes. If the speed exceeds the threshold, it reflects the instability or failure of the equipment. For example, if the motor or transmission system is faulty, it may cause abnormal speed. Especially when the equipment is under uneven load, jammed, or the mechanical parts are damaged, the speed may also show abnormal fluctuations.

[0068] The cumulative coefficient for operational anomalies is calculated using the following formula: Among them, YX jl For the cumulative coefficient of operational anomalies, [t] a ,t b [t] represents the time period during which the vibration acceleration signal is abnormal. c ,t d [t] represents the time period during which the speed signal is abnormal. e ,t f [This represents the time period during which the angle signal was abnormal.]

[0069] As can be seen from the formula, the larger the cumulative coefficient of abnormal operation, the more abnormal time the equipment accumulates, the greater the instability of the equipment operation, and the more likely there are problems such as overload, material jamming, mechanical damage or failure.

[0070] A comprehensive analysis of acoustic and operational characteristic information is conducted. An operational evaluation model is constructed using a weighted calculation of the acoustic spectral entropy anomaly coefficient, the breakage performance fluctuation coefficient, and the cumulative coefficient of operational anomalies. This model generates operational evaluation coefficients, calculated using the formula: YX pg =β1PS yc +β2BD ps +β3YX jl Among them, YX pg The coefficients are the operational evaluation coefficients, and β1, β2, and β3 are the proportional coefficients of the acoustic spectral entropy anomaly coefficient, the breakage performance fluctuation coefficient, and the operational anomaly cumulative coefficient, respectively. β1, β2, and β3 are all greater than 0.

[0071] A threshold for the operation evaluation coefficient is set. The operation evaluation coefficient of the cone crusher during operation within the monitoring range is compared with the threshold. If the operation evaluation coefficient is greater than the threshold, an early warning signal is generated, indicating that there is an abnormal operation or potential fault in the equipment. The early warning signal is issued in a timely manner to help the operator take corresponding maintenance and adjustment measures. If the operation evaluation coefficient is less than the threshold, no early warning signal is generated.

[0072] This invention utilizes various sensors, such as laser particle size analyzers, flow sensors, acoustic sensors, vibration acceleration sensors, speed sensors, and angle sensors, installed in key components like the feed inlet, adjusting ring assembly, fixed cone assembly, main shaft bearing housing, and eccentric steel sleeve. These sensors collect real-time information on material particle size, flow rate, vibration signals, rotational characteristics, and acoustic features. Through multi-dimensional monitoring and real-time data analysis, the operating status of the cone crusher is evaluated, ensuring that the equipment operates under optimal conditions, avoiding equipment failures, and reducing maintenance costs.

[0073] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0074] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0075] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0076] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0077] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0078] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0079] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An intelligent method for real-time assessment of the crushing status of a stone crusher, characterized in that, Specifically, the following steps are included: S1: Install laser particle size analyzers and flow sensors around the feed inlet and adjusting ring assembly to capture feed particle size and flow rate in real time. Arrange acoustic sensors near the fixed cone assembly to record crushing sounds. Utilize the rigidity of the beamless machine body to securely install vibration acceleration sensors on the main shaft bearing housing and frame. Arrange speed and angle sensors at the eccentric steel sleeve to collect rotational characteristics. S2: By analyzing the collected acoustic signals in the time and frequency domains, the acoustic characteristic information of the crusher's operation is obtained, and the working condition characteristic information of the crusher's operation is obtained based on the multi-dimensional monitoring data of the cone crusher's operating status. S3: Comprehensively analyze the acoustic and working condition characteristics of the crusher to evaluate its real-time operating status. The operating status of the crusher is evaluated by comparing preset thresholds. Acoustic characteristic information and operating condition characteristic information, including: Acoustic characteristic information is represented by the acoustic spectral entropy anomaly system, and operating condition characteristic information is represented by the crushing performance fluctuation coefficient and the cumulative coefficient of operational anomalies. The acoustic spectral entropy anomaly coefficient. The fluctuation coefficient represents the breakage behavior. This is the cumulative coefficient for operational anomalies.

2. The intelligent real-time crushing status assessment method for a stone crusher according to claim 1, characterized in that, The logic for obtaining the acoustic spectral entropy anomaly coefficient is as follows: Acoustic sensors arranged at the fixed cone assembly are used to collect acoustic signals captured by the sensors during the operation of the cone crusher within the monitoring range. These acoustic signals are then converted from the time domain to the frequency domain using Fourier transform. The expression for the Fourier transform is as follows: ;in, For frequency domain signals, It is a time-domain signal; The acoustic signal of the cone crusher during operation within the monitoring range is divided into low-frequency, mid-frequency, and high-frequency ranges. The power spectral density is calculated for each of the low-frequency, mid-frequency, and high-frequency ranges. The formula for calculating the power spectral density is as follows: ;in, For power spectral density, For frequency The normalized power spectral density value at the specified location, where N is the number of frequency components in the corresponding frequency range; The standard coefficients for spectral entropy monitoring in different frequency ranges are calculated using the following formula: ;in, Standard coefficients for monitoring spectral entropy in different frequency ranges. Let m be the power spectral density of the m-th frequency interval. Let be the reference power spectral density under normal operating conditions of the device in the m-th frequency interval. This is the critical value at which the device malfunctions in the m-th frequency range; The formula for calculating the acoustic spectral entropy anomaly coefficient is as follows: ;in, These are the weighting coefficients for each frequency range.

3. The intelligent real-time crushing status assessment method for a stone crusher according to claim 2, characterized in that, The logic for obtaining the fluctuation coefficient of the breakage performance is as follows: By installing a laser particle size analyzer, the particle size of the material at the feed inlet and discharge outlet is determined. The crushing ratio is obtained by comparing the particle size of the material at the feed inlet with that at the discharge outlet during the operation of the cone crusher within the monitoring range. The calculation formula is as follows: ;in, This refers to the crushing ratio of the cone crusher during operation within the monitoring range. For the particle size of the material at the feed port, The particle size of the material at the discharge port is k = 1, 2, 3, ..., K, where K is a positive integer and k is the number of the particle size collected by the laser particle size analyzer within the monitoring interval. Based on the crushing ratio of the cone crusher during operation within the monitoring range, the standard deviation and average value of the crushing ratio within the monitoring range are calculated, and these values ​​are denoted as: and ;in, , ; The formula for calculating the fluctuation coefficient of crushing performance is as follows: .

4. The intelligent real-time crushing status assessment method for a stone crusher according to claim 3, characterized in that, The logic for obtaining the cumulative coefficient of operational anomalies is as follows: Vibration acceleration, rotational speed, and angle signals of the cone crusher during its operation within the monitoring range are obtained using vibration acceleration sensors, rotational speed sensors, and angle sensors. These signals are then represented as follows: , as well as By setting threshold ranges for vibration acceleration, rotation speed, and angle signals, the time period during which the detected signal exceeds the threshold can be obtained. The cumulative coefficient for operational anomalies is calculated using the following formula: ;in, This refers to the time period during which the vibration acceleration signal is abnormal. This refers to the period during which the speed signal is abnormal. This refers to the time period during which the angle signal is abnormal.

5. The intelligent real-time crushing status evaluation method for a stone crusher according to claim 4, characterized in that, A comprehensive analysis of the acoustic and operational characteristics of the crusher is conducted, including: An operational evaluation model is constructed by weighting the acoustic spectral entropy anomaly coefficient, the breakage performance fluctuation coefficient, and the cumulative coefficient of operational anomalies, and operational evaluation coefficients are generated. The calculation formula for the operational evaluation coefficients is as follows: ;in, For operational evaluation coefficients, , , These are the proportional coefficients for the acoustic spectral entropy anomaly system, the breakage performance fluctuation coefficient, and the cumulative coefficient of operational anomalies, respectively. , , All are greater than 0.

6. The intelligent real-time crushing status evaluation method for a stone crusher according to claim 5, characterized in that, The operating status of the crusher is evaluated by comparing preset thresholds, including: Set an operating evaluation coefficient threshold. Compare the operating evaluation coefficient of the cone crusher with the operating evaluation coefficient threshold during operation within the monitoring range. If the operating evaluation coefficient is greater than the operating evaluation coefficient threshold, an early warning signal is generated. If the operating evaluation coefficient is less than the operating evaluation coefficient threshold, no early warning signal is generated.

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