Equipment ledger management system for coal mine production

Abstract

The invention discloses an equipment ledger management system for coal mine production, which relates to the technical field of coal mine equipment ledger management and comprises an equipment monitoring module, an equipment utilization rate calculation module, a fault judgment module, a fault recording module and an equipment fault evaluation module. The equipment monitoring module is used for setting a sensor to monitor coal mine equipment according to monitoring requirements to obtain working state data of the coal mine equipment; according to the equipment ledger management system for coal mine production, by arranging the fault recording module and the equipment fault evaluation module, fault parts can be recorded, whether the risk that the parts of coal mine equipment have defects is high or not is judged by analyzing fault records, and reminding is carried out when the defect risk is high; the fault component and the coal mine equipment can be analyzed, the defects of the coal mine equipment and the components thereof are determined, and whether the defects are performance defects or assembly defects of the components is determined.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mine equipment ledger management, and particularly relates to an equipment ledger management system for coal mine production. Background Art

[0002] Coal mine ledgers are mainly used to record and manage information such as coal mine production, equipment, personnel, and safety, ensuring operational compliance and safety. At the same time, it also helps monitor resource usage and improve management efficiency. The ledger management of coal mine equipment involves recording the basic information, usage conditions, maintenance records, and failure history of the equipment. This helps ensure the normal operation, timely maintenance, and replacement of the equipment, improving overall safety and production efficiency.

[0003] The prior art with the publication number CN118261137A discloses a unified management method for coal mine water control ledger data, including: building a configuration item - configuration rule association library based on all free configuration items and corresponding all free configuration rules in the built-in models of all ledger templates; generating a configuration rule selection box for the starting input data box based on the ledger template selection instruction, starting input instruction, and configuration item - configuration rule association library input by the user, and receiving the selected configuration rule input by the user for the starting input box; generating a configuration rule selection box for the latest to-be-typed box based on all currently obtained selected configuration rules until the user completes all data input work for selecting the ledger template, obtaining a ledger data model, and obtaining the statistical analysis report result of the ledger data model; to improve the management efficiency of coal mine water control ledger data.

[0004] However, the above prior art is mainly used to improve data management efficiency. During the process of coal mine equipment production work, various problems or damages may occur to coal mine equipment. The prior art also has the problem that it cannot improve the working efficiency of coal mine equipment by improving the coal mine equipment according to the problems and operation data that occur to the coal mine equipment. Summary of the Invention

[0005] The purpose of the present invention is to provide an equipment ledger management system for coal mine production to solve the above deficiencies in the prior art.

[0006] To achieve the above purpose, the present invention provides the following technical solution: An equipment ledger management system for coal mine production, including an equipment monitoring module, an equipment utilization rate calculation module, a fault judgment module, a fault record module, and an equipment fault assessment module;

[0007] The equipment monitoring module is used to set sensors to monitor coal mine equipment according to monitoring requirements, and obtain the working state data of the coal mine equipment, where the working state data of the coal mine equipment includes operation time data, operation power data, production data, performance data: such as temperature, pressure, voltage, current, speed, etc.;

[0008] The device utilization rate calculation module is used to calculate the device utilization rate of coal mine equipment based on the operation time data. Among them, the daily operation duration of coal mine equipment can be obtained from the operation time data, and the device utilization rate is calculated by the following formula:

[0009]

[0010] Among them, η is the device utilization rate, n is the total number of days of operation time data, i represents the i-th day, t is the operation duration, and t i represents the operation duration on the i-th day;

[0011] The fault judgment module is used to analyze the working state data based on a machine learning model to judge whether the coal mine equipment is faulty. Among them, the machine learning model can be an existing fault diagnosis algorithm;

[0012] The fault record module is used to obtain the judgment result of the fault judgment module and record it when the equipment is faulty to obtain the number of faults of each component of the coal mine equipment;

[0013] The equipment fault evaluation module is used to judge whether there are defects in the corresponding components based on the number of faults of each component of the coal mine equipment, and issue a reminder when it is judged that there are defects in the components.

[0014] Furthermore, the system further includes a device management module, which is used to manually control the coal mine equipment, and is also used to set the utilization rate threshold interval, obtain the device utilization rate obtained by the device utilization rate calculation module, and issue a reminder when the device utilization rate is not within the utilization rate threshold interval; in one embodiment, the device utilization rate interval is 75% (inclusive)-95% (inclusive). If the calculated average device utilization rate for the recently set number of days is 72% or 96%, a reminder is issued. Furthermore, when the utilization rate is 72%, the output of the coal mine equipment can be reduced to lower the output of the coal mine equipment, or the coal mine equipment can be reduced; when the utilization rate is 96%, consider increasing the coal mine equipment, or increasing the output of the coal mine equipment to increase the output.

[0015] Furthermore, the machine learning model is trained based on historical working state data, and the training includes the following steps:

[0016] Obtain historical working state data;

[0017] Set multiple fault labels corresponding to multiple faults. The fault labels are also used to indicate the faulty components. For example, if the first component is associated with the first fault label, the output of the first fault label indicates that the first component has a fault;

[0018] Associate the fault labels with the corresponding historical working state data;

[0019] Train a machine learning model based on historical working status data and corresponding fault labels, so that the machine learning model outputs corresponding predicted fault labels based on the input working status data;

[0020] Among them, during training, the historical working status data is used as the input of the machine learning model, and the corresponding fault label is used as the output; the present invention does not limit the specific machine learning model. First, select a suitable machine learning model according to needs, such as a decision tree model, a neural network model, etc., and select suitable activation functions and loss functions; divide the historical working status data into a training set and a test set according to a ratio of 6:4, and use the training set to train the machine learning model to obtain the specific parameters of the machine learning model; use the test set to test the accuracy of the output predicted fault label of the machine learning model after substituting the parameters. If the accuracy is qualified, determine the machine learning model and its parameters. If the accuracy is unqualified, adjust the parameters, activation functions, loss functions or the selected machine learning model and retrain.

[0021] Further, the equipment fault evaluation module is used to judge whether there are defects in the corresponding components based on the number of faults of each component of the coal mine equipment, including the following steps:

[0022] Judge whether the number of faults of a single component of a single coal mine equipment within a set period is greater than the set fault number threshold;

[0023] If so, judge whether the number of coal mine equipment with faults in the component within a set period is greater than the set equipment fault number threshold;

[0024] If not, perform the first defect detection on the components of the coal mine equipment with the number of faults greater than the set fault number threshold;

[0025] If so, perform the second defect detection on the components of all coal mine equipment.

[0026] Further, the first defect detection includes the following steps:

[0027] Maintain or replace the faulty components of the coal mine equipment;

[0028] Install vibration sensors on the coal mine equipment to monitor the vibration data of the components at the corresponding positions of the faulty components;

[0029] Based on the vibration data of the coal mine equipment with faults in the component, obtain the first vibration data set, and based on the vibration data of the coal mine equipment without faults in the component, obtain the second vibration data set;

[0030] Compare the first vibration data set with the second vibration data set to obtain a vibration error, and judge whether the vibration error is greater than the set vibration error threshold;

[0031] If so, there are assembly defects in the coal mine equipment.

[0032] Furthermore, the second defect detection includes the following steps:

[0033] Obtain a set number of faulty components and get the fault positions of each component;

[0034] Establish a three-dimensional model of the component;

[0035] Conduct modal analysis on the three-dimensional model of the component;

[0036] Based on the modal analysis, determine whether there is a resonance frequency match between the vibration frequency and the fault position;

[0037] If so, there are defects at the matching fault positions. That is, if there are fault positions where the vibration frequency matches the resonance frequency of the fault position, then these fault positions will bear greater stress at the matching vibration frequency, indicating insufficient rigidity at the fault positions and thus defects;

[0038] If not, there are no defects.

[0039] 1. Compared with the prior art, a device ledger management system for coal mine production provided by the present invention can determine whether a coal mine device is faulty and identify the faulty components by setting up a device monitoring module, a device utilization rate calculation module, and a fault judgment module based on the working state data of the coal mine device.

[0040] 2. Compared with the prior art, a device ledger management system for coal mine production provided by the present invention can record the faulty components and, by analyzing the fault records, determine whether the risk of defects in the components of the coal mine device is relatively high and give a reminder when the defect risk is relatively high by setting up a fault record module and a device fault assessment module.

[0041] 3. Compared with the prior art, a device ledger management system for coal mine production provided by the present invention can also analyze the faulty components and the coal mine device by setting up a fault record module and a device fault assessment module, determine the defects of the coal mine device and its components, and determine whether the defect is a performance defect or an assembly defect of the component. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0043] Figure 1 It is the system structure block diagram provided by the embodiment of the present invention;

[0044] Figure 2 This is a flowchart of the steps for determining whether there are defects in the components provided by the embodiments of the present invention. Detailed implementation manners

[0045] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0046] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality of" means two or more, unless otherwise specifically defined. In addition, the terms "mounted", "connected" and "connected" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected, or indirectly connected through an intermediate medium, and it may be the internal communication of two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0047] Hereinafter, example embodiments will be described more fully with reference to the accompanying drawings, but the example embodiments may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of this disclosure to those skilled in the art.

[0048] In the case of no conflict, the various embodiments of the present disclosure and the features in the embodiments may be combined with each other.

[0049] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0050] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. As used herein, the singular forms "a" and "the" are also intended to include the plural forms, unless the context clearly indicates otherwise. It will also be understood that when the terms "comprises" and / or "consists of" are used in this specification, it specifies the presence of the described features, wholes, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their groups.

[0051] The embodiments described herein may be described with reference to plan views and / or cross-sectional views by means of the ideal schematic diagrams of the present disclosure. Therefore, the example illustrations can be modified according to manufacturing techniques and / or tolerances. Therefore, the embodiments are not limited to the embodiments shown in the drawings, but include modifications of the configurations formed based on the manufacturing process. Therefore, the regions illustrated in the drawings have schematic attributes, and the shapes of the regions shown in the figures illustrate the specific shapes of the regions of the components, but are not intended to be restrictive.

[0052] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in common dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein.

[0053] Please refer to Figure 1 , a ledger management system for coal mine production equipment, including an equipment monitoring module, an equipment utilization rate calculation module, a fault judgment module, a fault record module, and an equipment fault assessment module;

[0054] The equipment monitoring module is used to set sensors to monitor coal mine equipment according to monitoring requirements, and obtain the working state data of the coal mine equipment, where the working state data of the coal mine equipment includes running time data, running power data, production data, and performance data: such as temperature, pressure, voltage, current, speed, etc.;

[0055] The equipment utilization rate calculation module is used to calculate the equipment utilization rate of the coal mine equipment based on the running time data. Among them, the daily running duration of the coal mine equipment can be obtained through the running time data, and the equipment utilization rate is calculated by the following formula:

[0056]

[0057] Among them, η is the equipment utilization rate, n is the total number of days of the running time data, i represents the i-th day, t is the running duration, and t i represents the running duration on the i-th day;

[0058] The fault judgment module is used to analyze the working state data based on a machine learning model to judge whether the coal mine equipment is faulty. Among them, the machine learning model can be an existing fault diagnosis algorithm;

[0059] The fault record module is used to obtain the judgment result of the fault judgment module and record it when the equipment fails, and obtain the number of faults of each component of the coal mine equipment;

[0060] The equipment failure assessment module is used to judge whether there are defects in the corresponding components based on the number of failures of each component of the coal mine equipment, and issue a reminder when it is judged that there are defects in the components.

[0061] In one embodiment, it is detected that the first component of a coal mine equipment has been damaged 4 times within a quarter, which is greater than the set failure times threshold of 3 times. Then, the number of other coal mine equipment with failures of the first component within this quarter is obtained. It is known that there are a total of 10 other coal mine equipment, and 4 coal mine equipment with failures of the first component within this quarter, which is greater than the set equipment failure number threshold of 3. Then, defect detection needs to be carried out on the first components of all coal mine equipment.

[0062] The equipment failure assessment module is used to judge whether there are defects in the corresponding components based on the number of failures of each component of the coal mine equipment, including the following steps:

[0063] (1) Judge whether the number of failures of a single component of a single coal mine equipment within a set period is greater than the set failure times threshold;

[0064] (2) If so, judge whether the number of coal mine equipment with component failures within a set period is greater than the set equipment failure number threshold;

[0065] (3) If not, conduct the first defect detection on the components of the coal mine equipment with the number of failures greater than the set failure times threshold; among them, the first defect detection includes the following steps:

[0066] A1. Maintain or replace the failed components of the coal mine equipment;

[0067] A2. Install vibration sensors on the coal mine equipment to monitor the vibration data of the components at the corresponding positions of the failed components;

[0068] A3. Obtain the first vibration data set based on the vibration data of the coal mine equipment with component failures, and obtain the second vibration data set based on the vibration data of the coal mine equipment without component failures;

[0069] A4. Compare the first vibration data set with the second vibration data set to obtain the vibration error, and judge whether the vibration error is greater than the set vibration error threshold;

[0070] A5. If so, there are assembly defects in the coal mine equipment.

[0071] (4) If so, conduct the second defect detection on the components of all coal mine equipment. The second defect detection includes the following steps:

[0072] B1. Obtain the components with a set number of failures to obtain the failure positions of each component;

[0073] B2. Establish a three-dimensional model of the component.

[0074] B3. Perform modal analysis on the three-dimensional model of the component;

[0075] B4. Based on modal analysis, determine whether there is a match between the vibration frequency and the resonance frequency of the fault location;

[0076] B5. If yes, the matching fault location has defects, that is, if there is a fault location whose vibration frequency matches the resonance frequency of the fault location, the fault location will be subjected to greater stress at the matching vibration frequency, and the rigidity of the fault location is insufficient, that is, the performance of the component has defects;

[0077] B6. If no, then there is no defect.

[0078] The system also includes an equipment management module for manually controlling coal mine equipment, and is also used to set a utilization threshold range, obtain the equipment utilization obtained by the equipment utilization calculation module, and issue a reminder when the equipment utilization is not within the utilization threshold range; in one embodiment, the equipment utilization range is 75% (inclusive)-95% (inclusive), and the average equipment utilization of the most recently set number of days is calculated to be 72% or 96%, then a reminder is issued, and further, at 72%, the output of the coal mine equipment can be lowered to reduce the output of the coal mine equipment, or the coal mine equipment can be reduced; at 96%, consideration is given to increasing and improving the coal mine equipment, or increasing the output of the coal mine equipment to increase production.

[0079] The machine learning model is trained based on historical work status data. The training includes the following steps:

[0080] (1) Obtain historical work status data;

[0081] (2) multiple fault labels are set to correspond to multiple faults. The fault labels are also used to indicate the faulty components. For example, a first component is associated with a first fault label, and then the output of the first fault label indicates that the first component has a fault.

[0082] (3) Associating the fault tag with the corresponding historical working status data;

[0083] (4) Based on the historical working status data and the corresponding fault labels, the machine learning model is trained so that the machine learning model outputs the corresponding predicted fault labels based on the input working status data;

[0084] Among them, during training, historical working state data is used as the input of the machine learning model, and the corresponding fault label is used as the output; the present invention does not limit the specific machine learning model. First, select a suitable machine learning model according to needs, such as a decision tree model, a neural network model, etc., and select suitable activation functions and loss functions; divide the historical working state data into a training set and a test set according to a ratio of 6:4, and use the training set to train the machine learning model to obtain the specific parameters of the machine learning model; use the test set to test the accuracy of the output of the machine learning model with the substituted parameters in predicting the fault label. If the accuracy is qualified, determine the machine learning model and its parameters. If the accuracy is unqualified, adjust the parameters, activation functions, loss functions or the selected machine learning model and retrain.

[0085] Only some exemplary embodiments of the present invention have been described by way of illustration above. Undoubtedly, for those of ordinary skill in the art, the described embodiments can be modified in various different ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of the claims of the present invention.

Claims

1. An equipment ledger management system for coal mine production, characterized in that: It includes a device monitoring module, a device utilization rate calculation module, a fault judgment module, a fault recording module, and a device fault assessment module; The device monitoring module is used to set sensors according to monitoring requirements to monitor coal mine equipment, and obtain the working state data of the coal mine equipment, where the working state data of the coal mine equipment includes running time data, running power data, output data, and performance data; The device utilization rate calculation module is used to calculate the device utilization rate of the coal mine equipment based on the running time data; The fault judgment module is used to analyze the working state data based on a machine learning model to judge whether the coal mine equipment is faulty; The fault recording module is used to obtain the judgment result of the fault judgment module and record it when the equipment fails, so as to obtain the number of faults of each component of the coal mine equipment; The device fault assessment module is used to judge whether the corresponding component has defects based on the number of faults of each component of the coal mine equipment, and issue a reminder when it is judged that there are defects in the component.

2. The equipment ledger management system for coal mine production according to claim 1, wherein: The system also includes a device management module, which is used to manually control the coal mine equipment, and is also used to set the utilization rate threshold range, obtain the device utilization rate obtained by the device utilization rate calculation module, and issue a reminder when the device utilization rate is not within the utilization rate threshold range.

3. The equipment ledger management system for coal mine production according to claim 1, wherein: The machine learning model is trained based on historical working state data, and the training includes the following steps: Obtain historical working state data; Set multiple fault labels corresponding to multiple faults, and the fault labels are also used to indicate the faulty components; Associate the fault labels with the corresponding historical working state data; Based on the historical working state data and the corresponding fault labels, train the machine learning model so that the machine learning model outputs the corresponding predicted fault labels based on the input working state data.

4. The equipment ledger management system for coal mine production according to claim 1, wherein: The device fault assessment module is used to judge whether the corresponding component has defects based on the number of faults of each component of the coal mine equipment, including the following steps: Judge whether the number of faults of a single component of a single coal mine equipment within a set period is greater than the set fault number threshold; If so, judge whether the number of coal mine equipment with faults in the component within a set period is greater than the set equipment fault number threshold; If not, perform the first defect detection on the components of the coal mine equipment with the number of faults greater than the set fault number threshold; If so, perform the second defect detection on the components of all coal mine equipment.

5. The equipment ledger management system for coal mine production according to claim 4, characterized in that: The first defect detection includes the following steps: Maintain or replace the faulty components of the coal mine equipment; Install vibration sensors on the coal mine equipment to monitor the vibration data of the components at the corresponding positions of the faulty components; Based on the vibration data of the coal mine equipment with faulty components, obtain the first vibration data set, and based on the vibration data of the coal mine equipment without faulty components, obtain the second vibration data set; Compare the first vibration data set with the second vibration data set to obtain a vibration error, and judge whether the vibration error is greater than the set vibration error threshold; If so, there are assembly defects in the coal mine equipment.

6. The equipment ledger management system for coal mine production according to claim 4, characterized in that: The second defect detection includes the following steps: Obtain the set number of faulty components and obtain the fault positions of each component; Establish a three-dimensional model of the component; Perform modal analysis on the 3D model of the component; Based on the modal analysis, determine whether there is a resonance frequency match between the vibration frequency and the resonance frequency of the fault location; If so, there are defects at the matching fault locations; If not, there are no defects.

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