Intelligent inspection method and system for open pit coal mine equipment

Abstract

The invention relates to the technical field of equipment inspection, and particularly discloses an intelligent inspection method and system for open-pit coal mine equipment, and the method comprises the following steps: S1, obtaining the temperature of equipment of the same type in a monitoring area at each time node; s2, acquiring a mean value of the temperature of the equipment at the current time node, calculating an evaluation difference value, and screening the monitoring equipment; acquiring evaluation difference values of the monitoring equipment on the next n time nodes, and judging whether the monitoring equipment is target equipment or not; s3, acquiring the environment temperature at the current time node, acquiring the temperature of the target equipment when the environment temperature belongs to a preset environment temperature range in the evaluation period, generating corresponding data points in the coordinate system, and performing fitting to generate a temperature change curve; and screening abnormal equipment according to the monotonicity of the temperature change curve. The method can predict the equipment possibly having the abnormity in advance, and guarantees the mining efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment inspection, and in particular to an intelligent inspection method and system for open-pit coal mine equipment. Background Art

[0002] Open-pit coal mines refer to coal layers deposited on the surface or shallow layers due to geographical changes. Open-pit coal mines are directly mined through open-pit mining. Open-pit coal mining equipment usually refers to a series of mechanical equipment used for open-pit coal mining. Such as transportation equipment: heavy trucks and dump trucks are used to transport the excavated coal from the mining site to the storage or processing site; auxiliary equipment: In addition to the main equipment, a series of auxiliary equipment is also required to support mining operations, such as lighting, ventilation, drainage and other equipment.

[0003] In order to ensure the stable operation of open-pit coal mine equipment and ensure that the mining progress is not affected, the inspection of open-pit coal mine equipment is particularly important. During the inspection process, the staff mainly rely on real-time monitoring of certain key parameters of the equipment when it is working, such as temperature, to evaluate the equipment status. By comparing with the preset safety threshold, the abnormal situation of the equipment can be discovered in time and corresponding measures can be taken to deal with it. However, when an abnormality is found in the equipment during the inspection, it is often necessary to stop the equipment immediately for further maintenance work, which will affect the overall mining efficiency. Based on this, an intelligent inspection method for open-pit coal mine equipment is provided to predict equipment that may have abnormalities in advance and ensure mining efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide an open-pit coal mine equipment intelligent inspection method and system to solve the above-mentioned technical problems.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] An open-pit coal mine equipment intelligent inspection method comprises the following steps:

[0007] S1: Setting a monitoring cycle, setting a number of time nodes at preset time intervals within the monitoring cycle, setting a monitoring area, and obtaining the temperature of the same type of equipment in the monitoring area at the time nodes;

[0008] S2: Obtain the average temperature of the device at the current time node, and calculate the evaluation difference Ci'=Ci-C', where Ci represents the temperature of the i-th device, and C' represents the average temperature. When the evaluation difference is greater than or equal to a preset value, the i-th device is determined as a monitoring device;

[0009] Setting the number of monitoring times n, and continuously obtaining the evaluation difference of the monitoring device at the next n time nodes, when the ratio of the evaluation difference being greater than or equal to a preset value exceeds a preset ratio threshold, determining the monitoring device as a target device;

[0010] S3: Obtain the ambient temperature T at the current time node, set the ambient temperature range [T-T', T+T'], set an evaluation period, obtain the temperature of the target device when the ambient temperature belongs to the ambient temperature range within the evaluation period, generate corresponding data points in the coordinate system, and fit the data points by the least squares method. The fitting formula is f(t), and a temperature change curve is generated;

[0011] The monotonicity of the temperature change curve is obtained, and devices with abnormalities are screened according to the monotonicity.

[0012] As a further solution of the present invention: in the step S1, in the process of obtaining the temperature, a number of detection points are set on the device at preset fixed distance intervals, and the average temperature of each detection point at the same time node is obtained as the temperature of the device.

[0013] As a further solution of the present invention: in the process of calculating the mean, when the difference between the temperature at a certain detection point and the mean is greater than a preset value, the temperature at the detection point is removed and the mean is recalculated.

[0014] As a further solution of the present invention: in the step S3, the process of screening devices that may have abnormalities according to the monotonicity specifically includes:

[0015] When the monotonicity is monotonically increasing, it is determined that the target device is abnormal;

[0016] When the monotonicity is monotonically decreasing, executing step S2 for the target device, and when the ratio of the evaluation difference being greater than or equal to the preset value exceeds a preset ratio threshold, determining that the target device is abnormal;

[0017] When the temperature change curve does not have monotonicity, the data points corresponding to the target device are obtained, and the variance of the temperature is calculated. When the variance is greater than or equal to a preset value, the maximum value T in the temperature change curve is obtained. max =max(f(t));

[0018] When the maximum value Tmax is greater than a preset safety threshold, it is determined that the target device is abnormal.

[0019] As a further solution of the present invention: when the temperature change curve does not have monotonicity and the variance is less than a preset value, the data points corresponding to the target device are obtained and the mean of the temperature is calculated. When the mean is greater than or equal to the preset value, it is determined that the target device is abnormal.

[0020] An open-pit coal mine equipment intelligent inspection system, comprising:

[0021] Data acquisition module: setting a monitoring period, setting a number of time nodes at preset time intervals within the monitoring period, setting a monitoring area, and obtaining the temperature of the same type of equipment in the monitoring area at the time nodes;

[0022] Initial judgment module: obtain the average temperature of the device at the current time node, and calculate the evaluation difference Ci'=Ci-C', where Ci represents the temperature of the i-th device, and C' represents the average temperature. When the evaluation difference is greater than or equal to the preset value, the i-th device is determined as a monitoring device;

[0023] Setting the number of monitoring times n, and continuously obtaining the evaluation difference of the monitoring device at the next n time nodes, when the ratio of the evaluation difference being greater than or equal to a preset value exceeds a preset ratio threshold, determining the monitoring device as a target device;

[0024] Judgment optimization module: obtain the ambient temperature T at the current time node, set the ambient temperature range [T-T', T+T'], set the evaluation period, obtain the temperature of the target device when the ambient temperature belongs to the ambient temperature range within the evaluation period, generate corresponding data points in the coordinate system, and fit the data points by the least squares method. The fitting formula is f(t), and a temperature change curve is generated;

[0025] The monotonicity of the temperature change curve is obtained, and devices with abnormalities are screened according to the monotonicity.

[0026] Beneficial effects of the present invention: In the present invention, firstly, the set monitoring area and monitoring cycle are obtained, and the temperature of the same type of equipment in the monitoring area is obtained; it is worth noting that the length of the monitoring cycle is one day, the ambient temperature in different regions may not be the same, and the heat dissipation performance of different types of equipment is not necessarily the same, so the temperature of the same type of equipment in the same region is selected as the basis for subsequent processing; then the target equipment is judged according to the comparison between the temperature and the mean; it can be understood that the temperature of the equipment that may have abnormalities is higher than that of normal equipment when working, so the abnormal equipment is preliminarily screened in this way; then the temperature change curve of the target equipment is obtained, and the monotonicity is obtained, and the equipment with abnormalities is screened according to the monotonicity; it can be understood that the length of the evaluation cycle is one month, and the abnormality of the equipment does not appear suddenly, but is caused by accumulation over time, so when the monotonicity is monotonically increasing, it can be judged that the equipment has abnormalities; when the monotonicity is monotonically decreasing, the target equipment is continuously monitored, and it is judged whether the target equipment has abnormalities. The present invention can predict the equipment that may have abnormalities in advance and ensure the mining efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The present invention will be further described below in conjunction with the accompanying drawings.

[0028] Figure 1 It is a flow chart of an intelligent inspection method for open-pit coal mine equipment according to the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] See also Figure 1 As shown, the present invention is an intelligent inspection method for open-pit coal mine equipment, comprising the following steps:

[0031] S1: Setting a monitoring cycle, setting a number of time nodes at preset time intervals within the monitoring cycle, setting a monitoring area, and obtaining the temperature of the same type of equipment in the monitoring area at the time nodes;

[0032] S2: Obtain the average temperature of the device at the current time node, and calculate the evaluation difference Ci'=Ci-C', where Ci represents the temperature of the i-th device, and C' represents the average temperature. When the evaluation difference is greater than or equal to a preset value, the i-th device is determined as a monitoring device;

[0033] Setting the number of monitoring times n, and continuously obtaining the evaluation difference of the monitoring device at the next n time nodes, when the ratio of the evaluation difference being greater than or equal to a preset value exceeds a preset ratio threshold, determining the monitoring device as a target device;

[0034] S3: Obtain the ambient temperature T at the current time node, set the ambient temperature range [T-T', T+T'], set an evaluation period, obtain the temperature of the target device when the ambient temperature belongs to the ambient temperature range within the evaluation period, generate corresponding data points in the coordinate system, and fit the data points by the least squares method. The fitting formula is f(t), and a temperature change curve is generated;

[0035] The monotonicity of the temperature change curve is obtained, and devices with abnormalities are screened according to the monotonicity.

[0036] It should be noted that first, the set monitoring area and monitoring period are obtained, and the temperature of the same type of equipment in the monitoring area is obtained; it is worth noting that the length of the monitoring period is one day, the ambient temperature in different regions may not be the same, and the heat dissipation performance of different types of equipment may not be the same, so the temperature of the same type of equipment in the same region is selected as the basis for subsequent processing; then the target device is judged based on the comparison between the temperature and the mean; it can be understood that the temperature of the equipment that may have abnormalities is higher than that of normal equipment when it is working, so the abnormal equipment is preliminarily screened in this way; then the temperature change curve of the target equipment is obtained, and the monotonicity is obtained, and the abnormal equipment is screened according to the monotonicity; it can be understood that the length of the evaluation period is one month, and the abnormality of the equipment does not appear suddenly, but is caused by accumulation over time, so when the monotonicity is monotonically increasing, it can be judged that the equipment has an abnormality; when the monotonicity is monotonically decreasing, the target equipment is continuously monitored and it is judged whether the target equipment has an abnormality.

[0037] In another preferred implementation of the present invention, in the step S1, in the process of obtaining the temperature, a number of detection points are set on the device at preset fixed distance intervals, and the average temperature of each detection point at the same time node is obtained as the temperature of the device.

[0038] It is worth noting that the purpose of doing this is to reduce errors. The temperatures at different locations on the device may not be the same. For example, the temperature near the heating area may be higher. Therefore, the error is reduced by calculating the mean.

[0039] In another preferred implementation of the present invention, during the process of calculating the mean, when the difference between the temperature at a certain detection point and the mean is greater than a preset value, the temperature at the detection point is removed and the mean is recalculated.

[0040] It is understandable that the purpose of doing this is to reduce errors. When the difference between the temperature at a certain detection point and the mean is greater than the preset value, it means that the difference between the temperature at the corresponding detection point and the mean is too large, which may be caused by measurement errors and other reasons. Therefore, the temperature at the detection point is discarded and the mean is recalculated to improve the accuracy of subsequent processing.

[0041] In another preferred implementation of the present invention, in step S3, the process of screening devices that may have abnormalities according to the monotonicity specifically includes:

[0042] When the monotonicity is monotonically increasing, it is determined that the target device is abnormal;

[0043] When the monotonicity is monotonically decreasing, executing step S2 for the target device, and when the ratio of the evaluation difference being greater than or equal to the preset value exceeds a preset ratio threshold, determining that the target device is abnormal;

[0044] When the temperature change curve does not have monotonicity, the data points corresponding to the target device are obtained, and the variance of the temperature is calculated. When the variance is greater than or equal to a preset value, the maximum value T in the temperature change curve is obtained. max =max(f(t));

[0045] When the maximum value Tmax is greater than a preset safety threshold, it is determined that the target device is abnormal.

[0046] In another preferred implementation of the present invention, when the temperature change curve does not have monotonicity and the variance is less than a preset value, the data points corresponding to the target device are obtained and the mean of the temperature is calculated. When the mean is greater than or equal to the preset value, it is determined that the target device is abnormal.

[0047] An open-pit coal mine equipment intelligent inspection system, comprising:

[0048] Data acquisition module: setting a monitoring period, setting a number of time nodes at preset time intervals within the monitoring period, setting a monitoring area, and obtaining the temperature of the same type of equipment in the monitoring area at the time nodes;

[0049] Initial judgment module: obtain the average temperature of the device at the current time node, and calculate the evaluation difference Ci'=Ci-C', where Ci represents the temperature of the i-th device, and C' represents the average temperature. When the evaluation difference is greater than or equal to the preset value, the i-th device is determined as a monitoring device;

[0050] Setting the number of monitoring times n, and continuously obtaining the evaluation difference of the monitoring device at the next n time nodes, when the ratio of the evaluation difference being greater than or equal to a preset value exceeds a preset ratio threshold, determining the monitoring device as a target device;

[0051] Judgment optimization module: obtain the ambient temperature T at the current time node, set the ambient temperature range [T-T', T+T'], set the evaluation period, obtain the temperature of the target device when the ambient temperature belongs to the ambient temperature range within the evaluation period, generate corresponding data points in the coordinate system, and fit the data points by the least squares method. The fitting formula is f(t), and a temperature change curve is generated;

[0052] The monotonicity of the temperature change curve is obtained, and devices with abnormalities are screened according to the monotonicity.

[0053] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An intelligent inspection method for open-pit coal mine equipment, characterized in that: The following steps are involved: S1: Setting a monitoring cycle, setting a number of time nodes at preset time intervals within the monitoring cycle, setting a monitoring area, and obtaining the temperature of the same type of equipment in the monitoring area at the time nodes; S2: Obtain the average temperature of the device at the current time node, and calculate the evaluation difference Ci'=Ci-C', where Ci represents the temperature of the i-th device, and C' represents the average temperature. When the evaluation difference is greater than or equal to a preset value, the i-th device is determined to be a monitoring device; Setting the number of monitoring times n, and continuously obtaining the evaluation difference of the monitoring device at the next n time nodes, when the ratio of the evaluation difference being greater than or equal to a preset value exceeds a preset ratio threshold, determining the monitoring device as a target device; S3: Obtain the ambient temperature T at the current time node, set the ambient temperature range [T-T', T+T'], set an evaluation period, obtain the temperature of the target device when the ambient temperature belongs to the ambient temperature range within the evaluation period, generate corresponding data points in the coordinate system, and fit the data points by the least squares method. The fitting formula is f(t), and a temperature change curve is generated; The monotonicity of the temperature change curve is obtained, and devices with abnormalities are screened according to the monotonicity.

2. The method for intelligent inspection of open-pit coal mine equipment according to claim 1, characterized in that: In the step S1, in the process of obtaining the temperature, a number of detection points are set on the device at preset fixed distance intervals, and the average temperature of each detection point at the same time node is obtained as the temperature of the device.

3. The method for intelligent inspection of open-pit coal mine equipment according to claim 2, characterized in that: In the process of calculating the mean, if the difference between the temperature at a certain detection point and the mean is greater than a preset value, the temperature at the detection point is removed and the mean is recalculated.

4. The method for intelligent inspection of open-pit coal mine equipment according to claim 1, characterized in that: In step S3, the process of screening devices that may have abnormalities according to the monotonicity specifically includes: When the monotonicity is monotonically increasing, it is determined that the target device is abnormal; When the monotonicity is monotonically decreasing, executing step S2 for the target device, and when the ratio of the evaluation difference being greater than or equal to the preset value exceeds a preset ratio threshold, determining that the target device is abnormal; When the temperature change curve does not have monotonicity, the data points corresponding to the target device are obtained, and the variance of the temperature is calculated. When the variance is greater than or equal to a preset value, the maximum value T in the temperature change curve is obtained. max =max(f(t)); When the maximum value Tmax is greater than a preset safety threshold, it is determined that the target device is abnormal.

5. The method for intelligent inspection of open-pit coal mine equipment according to claim 4, characterized in that: When the temperature change curve does not have monotonicity and the variance is less than a preset value, the data points corresponding to the target device are obtained and the mean of the temperature is calculated. When the mean is greater than or equal to the preset value, it is determined that the target device is abnormal.

6. An open-pit coal mine equipment intelligent inspection system, characterized in that: include: Data acquisition module: setting a monitoring period, setting a number of time nodes at preset time intervals within the monitoring period, setting a monitoring area, and obtaining the temperature of the same type of equipment in the monitoring area at the time nodes; Initial judgment module: obtain the average temperature of the device at the current time node, and calculate the evaluation difference Ci'=Ci-C', where Ci represents the temperature of the i-th device, and C' represents the average temperature. When the evaluation difference is greater than or equal to the preset value, the i-th device is determined as a monitoring device; Setting the number of monitoring times n, and continuously obtaining the evaluation difference of the monitoring device at the next n time nodes, when the ratio of the evaluation difference being greater than or equal to a preset value exceeds a preset ratio threshold, determining the monitoring device as a target device; Judgment optimization module: obtain the ambient temperature T at the current time node, set the ambient temperature range [T-T', T+T'], set the evaluation period, obtain the temperature of the target device when the ambient temperature belongs to the ambient temperature range within the evaluation period, generate corresponding data points in the coordinate system, and fit the data points by the least squares method. The fitting formula is f(t), and a temperature change curve is generated; The monotonicity of the temperature change curve is obtained, and devices with abnormalities are screened according to the monotonicity.

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