A coal mill intelligent adjustment control system and method

Abstract

The present application relates to the technical field of coal mill monitoring and control, and discloses an intelligent adjustment and control system and method for a coal mill, the system comprising: a monitoring module, used for setting characteristic components of a coal mill according to a historical vibration parameter set and establishing a plurality of monitoring sub-areas; a construction module, used for constructing a vibration-state evaluation model, and generating a state evaluation value for each monitoring sub-area; a prediction module, used for setting a monitoring time node according to the state evaluation value, collecting real-time vibration parameters according to the monitoring time node and generating an abnormality probability; an adjustment module, used for determining the comprehensive operating state of the coal mill according to the abnormality probability, generating an adjustment instruction and sending an alarm signal, by monitoring the vibration parameters of characteristic components of the coal mill, determining the abnormality probability and determining the operating state of the coal mill, formulating an adjustment instruction according to the operating state, and sending an alarm signal according to the adjusted vibration parameters, thereby improving the accuracy of the vibration monitoring data of the coal mill and reducing the equipment operation and maintenance costs.

Description

Technical Field

[0001] The present application relates to the technical field of coal mill monitoring and control, and in particular to an intelligent adjustment control system and method for a coal mill. Background Art

[0002] Coal mill is a machine that breaks coal into powder and grinds it into coal powder. It is an important auxiliary equipment for coal powder furnace. However, there is always vibration in the operation of coal mill. For example, hard debris and iron objects in the raw coal enter the coal mill, or the grinding parts of the coal mill fall off and are damaged, or the thickness of the coal seam is not appropriate, which will cause abnormal vibration. If the coal mill is in a vibrating state for a long time, it will cause itself and related equipment to be scrapped.

[0003] In the prior art, the vibration signal of the coal mill is usually collected to determine whether the coal mill operation status is abnormal based on the coal mill vibration signal. However, the vibration signal contains a lot of noise and cannot truly reflect the actual operation status of the coal mill, so it cannot be adjusted in time, increasing the operation and maintenance cost and reducing the reliability and availability of the equipment. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides an intelligent adjustment control system and method for a coal mill. By monitoring the vibration parameters of multiple characteristic components of the coal mill, the abnormal probability of each characteristic component is determined and a comprehensive analysis is performed, the comprehensive operating status of the coal mill is determined, and reasonable adjustment instructions are formulated according to the comprehensive operating status. It is also determined whether to send an alarm signal based on the adjusted vibration parameters, thereby improving the accuracy of the coal mill vibration monitoring data, reducing the equipment operation and maintenance costs, and preventing serious production equipment accidents.

[0005] In some embodiments of the present application, a coal mill intelligent adjustment control system and method are provided, the system comprising:

[0006] A monitoring module, used for setting characteristic components of the coal mill according to a historical vibration parameter set, and establishing a plurality of monitoring sub-areas according to the characteristic components;

[0007] A construction module, used to construct a vibration-state evaluation model, and generate a state evaluation value of each monitoring sub-area according to the vibration-state prediction model;

[0008] A prediction module is used to set a monitoring time node of a corresponding monitoring sub-area according to the state evaluation value, collect real-time vibration parameters of the corresponding component according to the monitoring time node, and generate an abnormal probability of the corresponding monitoring sub-area;

[0009] The adjustment module is used to determine the comprehensive operating state of the coal mill according to the abnormal probability of each monitoring sub-area, generate an adjustment instruction according to the comprehensive operating state, and determine whether to send an alarm signal.

[0010] In some embodiments of the present application, characteristic components of a coal mill are set according to a historical vibration parameter set, including:

[0011] The historical vibration parameter set includes historical vibration parameters of several coal mill components, and the historical vibration parameters include historical vibration frequency, historical vibration amplitude and historical vibration phase;

[0012] Generate a normal vibration parameter interval and an abnormal vibration parameter interval of a current component according to historical vibration parameters of the coal mill component;

[0013] When the historical vibration parameter is in the abnormal vibration parameter interval, determining a first correlation between the current component and other components, and determining a second correlation between the current component and the coal mill operating state according to the historical adjustment instructions corresponding to the historical vibration parameter;

[0014] A comprehensive correlation m of the components is generated according to the first correlation and the second correlation, and components whose comprehensive correlation is greater than a preset correlation threshold are set as characteristic components of the coal mill.

[0015] In some embodiments of the present application, generating a state evaluation value of each monitoring sub-area according to the vibration-state evaluation model includes:

[0016] Divide the normal vibration parameter interval of the component into multiple normal vibration parameter sub-intervals according to the operating stage factors, and configure a state coefficient e for each normal vibration parameter sub-interval;

[0017] Dividing the abnormal vibration parameter interval into a plurality of abnormal vibration parameter subintervals according to the divided time periods corresponding to the normal vibration parameter subintervals, and calculating a first critical difference amount and a second critical difference amount between the abnormal vibration parameter subinterval and the corresponding normal vibration parameter subinterval;

[0018] Generate an initial state evaluation value A1 according to the first critical difference and the second critical difference, and generate a historical state evaluation value a of the historical vibration parameter of each component according to the initial state evaluation value A1, the state coefficient e and the comprehensive correlation m of the corresponding component;

[0019] When the historical vibration parameter is in the abnormal vibration parameter sub-interval, a=A1*m, and when the historical vibration parameter is in the normal vibration parameter sub-interval, a=e*m;

[0020] A vibration-state evaluation model is constructed based on historical vibration parameters and corresponding historical state evaluation values. The vibration parameters of each component in a preset period of time are input into the vibration-state evaluation model to generate a state evaluation value for the corresponding monitoring sub-area.

[0021] In some embodiments of the present application, setting a monitoring time node corresponding to a monitoring sub-area according to a state evaluation value includes:

[0022] According to the relationship between the state evaluation value and the preset state evaluation value interval, the time interval t between the current monitoring time node and the next monitoring time node of the monitoring sub-area is set;

[0023] Presetting a first preset state evaluation value interval, a second preset state evaluation value interval, a third preset state evaluation value interval and a fourth preset state evaluation value interval;

[0024] When the state evaluation value is in the first preset state evaluation value interval, the fourth preset time interval t4 is set as the time interval t between the current monitoring time node and the next monitoring time node, that is, t=t4;

[0025] When the state evaluation value is in the second preset state evaluation value interval, the third preset time interval t3 is set as the time interval t between the current monitoring time node and the next monitoring time node, that is, t=t3;

[0026] When the state evaluation value is in the third preset state evaluation value interval, the second preset time interval t2 is set as the time interval t between the current monitoring time node and the next monitoring time node, that is, t=t2;

[0027] When the state evaluation value is in the fourth preset state evaluation value interval, the first preset time interval t1 is set as the time interval t between the current monitoring time node and the next monitoring time node, that is, t=t1;

[0028] Among them, t1<t2<t3<t4.

[0029] In some embodiments of the present application, generating an abnormal probability corresponding to a monitoring sub-area includes:

[0030] Establish life prediction model for each component;

[0031] Generate a life risk coefficient v for each component according to the life prediction model;

[0032] Compare the real-time vibration parameters with the preset vibration parameter intervals of the corresponding components, and generate the initial abnormal probability p1 of each monitoring sub-area according to the comparison results;

[0033] According to the life risk coefficient of each component and the initial abnormal probability of the corresponding monitoring sub-area, the abnormal probability P of each monitoring sub-area is generated, where P=p1*v.

[0034] In some embodiments of the present application, determining the comprehensive operating status of the coal mill includes:

[0035] Generate the comprehensive operation quantitative value K of the coal mill according to the abnormal probability P of each monitoring sub-area and the comprehensive correlation m of the corresponding components;

[0036]

[0037] Among them, Pi is the abnormal probability of the ith monitoring sub-area, and mi is the comprehensive correlation of the ith monitoring sub-area;

[0038] Presetting a first preset operation quantization value interval, a second preset operation quantization value interval and a third preset operation quantization value interval;

[0039] When the comprehensive operation quantization value K is in the first preset operation quantization value interval, it is determined that the comprehensive operation state of the coal mill is in a good state;

[0040] When the comprehensive operation quantization value K is in the second preset operation quantization value interval, it is determined that the comprehensive operation state of the coal mill is a general state;

[0041] When the comprehensive operation quantization value K is in the third preset operation quantization value interval, it is determined that the comprehensive operation state of the coal mill is an abnormal state.

[0042] In some embodiments of the present application, generating an adjustment instruction according to the comprehensive operating status includes:

[0043] When the comprehensive operating status of the coal mill is in a good state, no adjustment instruction is generated;

[0044] When the comprehensive operation state of the coal mill is a general state, a primary adjustment instruction is generated, wherein the primary adjustment instruction is to correct the operation load Y of the coal mill according to the first correction coefficient c1, and the corrected operation load is Y*c1;

[0045] When the comprehensive operating state of the coal mill is abnormal, a secondary adjustment instruction is generated, which corrects the operating load Y of the coal mill according to the second correction coefficient c2. The corrected operating load is Y*c2, where c1>c2.

[0046] In some embodiments of the present application, determining whether to send an alarm signal includes:

[0047] Control the coal mill according to the adjustment instruction and obtain the adjusted vibration parameters of each monitoring sub-area after adjustment, compare the adjusted vibration parameters with the corresponding preset vibration parameter interval, and generate the trend of the adjusted vibration parameters;

[0048] If the comprehensive operating state of the coal mill is a normal state and the trend of the adjusted vibration parameter is the first trend, no alarm signal is sent;

[0049] If the comprehensive operating state of the coal mill is in a normal state and the trend of the adjusted vibration parameter is the second trend, a first-level alarm signal is sent;

[0050] If the comprehensive operating state of the coal mill is in a normal state and the trend of the adjusted vibration parameter is the third trend, a secondary alarm signal is sent;

[0051] If the comprehensive operating state of the coal mill is abnormal and the trend of the adjusted vibration parameter is the first trend, no alarm signal is sent;

[0052] If the comprehensive operating state of the coal mill is abnormal and the trend of the adjusted vibration parameter is the second trend, a secondary alarm signal is sent;

[0053] If the comprehensive operating state of the coal mill is abnormal and the trend of the adjusted vibration parameters is the third trend, a third-level alarm signal is sent and the mill is shut down.

[0054] In some embodiments of the present application, a coal mill intelligent adjustment and control method is also included:

[0055] Set characteristic components of the coal mill according to the historical vibration parameter set, and establish multiple monitoring sub-areas according to the characteristic components;

[0056] Constructing a vibration-state evaluation model, and generating a state evaluation value of each monitoring sub-area according to the vibration-state prediction model;

[0057] According to the state evaluation value, a monitoring time node of the corresponding monitoring sub-area is set, and the real-time vibration parameters of the corresponding components are collected according to the monitoring time node to generate the abnormal probability of the corresponding monitoring sub-area;

[0058] The comprehensive operating status of the coal mill is determined according to the abnormal probability of each monitoring sub-area, and an adjustment instruction is generated according to the comprehensive operating status, and it is determined whether to send an alarm signal.

[0059] Compared with the prior art, the intelligent adjustment control system and method of a coal mill in the embodiment of the present application have the following beneficial effects:

[0060] By monitoring the vibration parameters of multiple characteristic components of the coal mill, determining the abnormal probability of each characteristic component and conducting a comprehensive analysis, the comprehensive operating status of the coal mill is determined, and reasonable adjustment instructions are formulated based on the comprehensive operating status. Based on the adjusted vibration parameters, it is determined whether to send an alarm signal, thereby improving the accuracy of the coal mill vibration monitoring data, reducing equipment operation and maintenance costs, and preventing serious production equipment accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a schematic diagram of a coal mill intelligent adjustment and control system in a preferred embodiment of the present application;

[0062] Figure 2 It is a flow chart of a coal mill intelligent adjustment and control method in the preferred embodiment of the present application. DETAILED DESCRIPTION

[0063] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application but are not intended to limit the scope of the present application.

[0064] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0065] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0066] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0067] like Figure 1 As shown, a coal mill intelligent adjustment and control system according to a preferred embodiment of the present application includes:

[0068] A monitoring module, used for setting characteristic components of the coal mill according to a historical vibration parameter set, and establishing a plurality of monitoring sub-areas according to the characteristic components;

[0069] A construction module, used to construct a vibration-state evaluation model, and generate a state evaluation value of each monitoring sub-area according to the vibration-state prediction model;

[0070] A prediction module is used to set a monitoring time node of a corresponding monitoring sub-area according to the state evaluation value, collect real-time vibration parameters of the corresponding component according to the monitoring time node, and generate an abnormal probability of the corresponding monitoring sub-area;

[0071] The adjustment module is used to determine the comprehensive operating state of the coal mill according to the abnormal probability of each monitoring sub-area, generate an adjustment instruction according to the comprehensive operating state, and determine whether to send an alarm signal.

[0072] In some embodiments of the present application, characteristic components of a coal mill are set according to a historical vibration parameter set, including:

[0073] The historical vibration parameter set includes historical vibration parameters of several coal mill components, and the historical vibration parameters include historical vibration frequency, historical vibration amplitude and historical vibration phase;

[0074] Generate a normal vibration parameter interval and an abnormal vibration parameter interval of a current component according to historical vibration parameters of the coal mill component;

[0075] When the historical vibration parameter is in the abnormal vibration parameter interval, determining a first correlation between the current component and other components, and determining a second correlation between the current component and the coal mill operating state according to the historical adjustment instructions corresponding to the historical vibration parameter;

[0076] A comprehensive correlation m of the components is generated according to the first correlation and the second correlation, and components whose comprehensive correlation is greater than a preset correlation threshold are set as characteristic components of the coal mill.

[0077] In this embodiment, a first correlation is generated based on the probability of abnormal vibration of the current component causing other components during historical operation and the importance of the corresponding components. The importance is set in advance. The historical adjustment instructions include the degree of adjustment of the operating load of the coal mill and whether to shut down the mill. The operating status of the coal mill after the historical adjustment instructions is compared with the operating status of the coal mill before the adjustment, and a second correlation is generated based on the comparison results.

[0078] In this embodiment, characteristic components and monitoring sub-areas are set according to the comprehensive correlation of multiple components of the coal mill, multiple monitoring devices (vibration sensors) are set in the monitoring sub-areas, and the abnormal probability of the corresponding components is obtained according to the vibration parameters of the monitoring sub-areas, thereby laying a data foundation for the subsequent determination of the operating status of the coal mill, ensuring the reliability of the data and reducing the safety risks of the coal mill.

[0079] In some embodiments of the present application, generating a state evaluation value of each monitoring sub-area according to the vibration-state evaluation model includes:

[0080] Divide the normal vibration parameter interval of the component into multiple normal vibration parameter sub-intervals according to the operating stage factors, and configure a state coefficient e for each normal vibration parameter sub-interval;

[0081] Dividing the abnormal vibration parameter interval into a plurality of abnormal vibration parameter subintervals according to the divided time periods corresponding to the normal vibration parameter subintervals, and calculating a first critical difference amount and a second critical difference amount between the abnormal vibration parameter subinterval and the corresponding normal vibration parameter subinterval;

[0082] Generate an initial state evaluation value A1 according to the first critical difference and the second critical difference, and generate a historical state evaluation value a of the historical vibration parameter of each component according to the initial state evaluation value A1, the state coefficient e and the comprehensive correlation m of the corresponding component;

[0083] When the historical vibration parameter is in the abnormal vibration parameter sub-interval, a=A1*m, and when the historical vibration parameter is in the normal vibration parameter sub-interval, a=e*m;

[0084] A vibration-state evaluation model is constructed based on historical vibration parameters and corresponding historical state evaluation values. The vibration parameters of each component in a preset period of time are input into the vibration-state evaluation model to generate a state evaluation value for the corresponding monitoring sub-area.

[0085] In this embodiment, the optimal vibration interval of the normal vibration parameter subinterval is determined based on the historical vibration parameters and the corresponding historical operating factors, the normal vibration parameter subinterval is compared with the optimal vibration interval, and the state coefficient is set according to the comparison result. The greater the distance between the normal vibration parameter subinterval and the optimal vibration interval, the greater the state coefficient, that is, the more the normal vibration parameter subinterval deviates from the optimal vibration interval, the greater the historical state evaluation value, and the greater the probability of abnormal vibration.

[0086] In this embodiment, when the first critical difference amount and the second critical difference amount are larger, the initial state evaluation value is larger, the historical state evaluation value is larger, that is, the abnormal vibration is larger.

[0087] In some embodiments of the present application, setting a monitoring time node corresponding to a monitoring sub-area according to a state evaluation value includes:

[0088] According to the relationship between the state evaluation value and the preset state evaluation value interval, the time interval t between the current monitoring time node and the next monitoring time node of the monitoring sub-area is set;

[0089] Presetting a first preset state evaluation value interval, a second preset state evaluation value interval, a third preset state evaluation value interval and a fourth preset state evaluation value interval;

[0090] When the state evaluation value is in the first preset state evaluation value interval, the fourth preset time interval t4 is set as the time interval t between the current monitoring time node and the next monitoring time node, that is, t=t4;

[0091] When the state evaluation value is in the second preset state evaluation value interval, the third preset time interval t3 is set as the time interval t between the current monitoring time node and the next monitoring time node, that is, t=t3;

[0092] When the state evaluation value is in the third preset state evaluation value interval, the second preset time interval t2 is set as the time interval t between the current monitoring time node and the next monitoring time node, that is, t=t2;

[0093] When the state evaluation value is in the fourth preset state evaluation value interval, the first preset time interval t1 is set as the time interval t between the current monitoring time node and the next monitoring time node, that is, t=t1;

[0094] Among them, t1<t2<t3<t4.

[0095] In this embodiment, when the state evaluation value is larger, the abnormal vibration is larger and the failure is more likely to occur, that is, the time interval between monitoring time nodes should be shorter, so as to solve the hidden dangers of coal mill failure in time and reduce operation and maintenance costs.

[0096] In some embodiments of the present application, generating an abnormal probability corresponding to a monitoring sub-area includes:

[0097] Establish life prediction model for each component;

[0098] Generate a life risk coefficient v for each component according to the life prediction model;

[0099] Compare the real-time vibration parameters with the preset vibration parameter intervals of the corresponding components, and generate the initial abnormal probability p1 of each monitoring sub-area according to the comparison results;

[0100] According to the life risk coefficient of each component and the initial abnormal probability of the corresponding monitoring sub-area, the abnormal probability P of each monitoring sub-area is generated, where P=p1*v.

[0101] In this embodiment, the lifespans of components involved in multiple monitoring sub-areas of the coal mill are different. The life prediction model obtains the remaining lifespan of the components involved in the current monitoring sub-area, and generates a life risk coefficient based on the remaining lifespan. The lower the remaining lifespan, the greater the life risk coefficient. The value range of v is (1, 1.35).

[0102] In this embodiment, the abnormal probability is obtained based on the life risk coefficient and the initial abnormal probability of each monitoring sub-area, which greatly improves the status accuracy of the components involved in each monitoring sub-area, reduces the safety risk of the coal mill, lays a data foundation for the subsequent determination of the comprehensive operating status of the coal mill, and improves the reliability of normal operation of the equipment.

[0103] In some embodiments of the present application, determining the comprehensive operating status of the coal mill includes:

[0104] Generate the comprehensive operation quantitative value K of the coal mill according to the abnormal probability P of each monitoring sub-area and the comprehensive correlation m of the corresponding components;

[0105]

[0106] Among them, Pi is the abnormal probability of the ith monitoring sub-area, and mi is the comprehensive correlation of the ith monitoring sub-area;

[0107] Presetting a first preset operation quantization value interval, a second preset operation quantization value interval and a third preset operation quantization value interval;

[0108] When the comprehensive operation quantization value K is in the first preset operation quantization value interval, it is determined that the comprehensive operation state of the coal mill is in a good state;

[0109] When the comprehensive operation quantization value K is in the second preset operation quantization value interval, it is determined that the comprehensive operation state of the coal mill is a general state;

[0110] When the comprehensive operation quantization value K is in the third preset operation quantization value interval, it is determined that the comprehensive operation state of the coal mill is an abnormal state.

[0111] In this embodiment, the first preset operating quantization value interval is smaller than the second preset operating quantization value interval, and the second preset operating quantization value interval is smaller than the third preset operating quantization value interval. When the comprehensive operating quantization value is larger, the operating state of the coal mill is worse. Reasonable adjustment instructions are formulated according to the operating state to ensure the normal operation of the equipment.

[0112] In some embodiments of the present application, generating an adjustment instruction according to the comprehensive operating status includes:

[0113] When the comprehensive operating status of the coal mill is in a good state, no adjustment instruction is generated;

[0114] When the comprehensive operation state of the coal mill is a general state, a primary adjustment instruction is generated, wherein the primary adjustment instruction is to correct the operation load Y of the coal mill according to the first correction coefficient c1, and the corrected operation load is Y*c1;

[0115] When the comprehensive operating state of the coal mill is abnormal, a secondary adjustment instruction is generated, which corrects the operating load Y of the coal mill according to the second correction coefficient c2. The corrected operating load is Y*c2, where c1>c2.

[0116] In this embodiment, when the comprehensive operation state of the coal mill is a normal state or an abnormal state, a reasonable correction coefficient is selected to correct the operation load, automatically reduce the operation load, ensure normal production and equipment safety, and actively alarm and shut down when the load reduction cannot solve the abnormal vibration to ensure safe operation. This reduces the maintenance cost and spare parts cost of the equipment, reduces the equipment operation and maintenance cost, and prevents the occurrence of vicious production equipment accidents.

[0117] In some embodiments of the present application, determining whether to send an alarm signal includes:

[0118] Control the coal mill according to the adjustment instruction and obtain the adjusted vibration parameters of each monitoring sub-area after adjustment, compare the adjusted vibration parameters with the corresponding preset vibration parameter interval, and generate the trend of the adjusted vibration parameters;

[0119] If the comprehensive operating state of the coal mill is a normal state and the trend of the adjusted vibration parameter is the first trend, no alarm signal is sent;

[0120] If the comprehensive operating state of the coal mill is in a normal state and the trend of the adjusted vibration parameter is the second trend, a first-level alarm signal is sent;

[0121] If the comprehensive operating state of the coal mill is in a normal state and the trend of the adjusted vibration parameter is the third trend, a secondary alarm signal is sent;

[0122] If the comprehensive operating state of the coal mill is abnormal and the trend of the adjusted vibration parameter is the first trend, no alarm signal is sent;

[0123] If the comprehensive operating state of the coal mill is abnormal and the trend of the adjusted vibration parameter is the second trend, a secondary alarm signal is sent;

[0124] If the comprehensive operating state of the coal mill is abnormal and the trend of the adjusted vibration parameters is the third trend, a third-level alarm signal is sent and the mill is shut down.

[0125] In this embodiment, the vibration parameters of the adjacent time periods after the adjustment instruction are obtained, and the vibration parameters are set as the adjustment vibration parameters in chronological order. According to the relationship between the adjacent vibration parameters and the preset vibration parameter interval in the adjustment vibration parameters, the trend of the adjustment vibration parameters is judged. For example, the front vibration parameter and the rear vibration parameter are adjacent vibration parameters, the difference between the front vibration parameter and the preset vibration parameter interval is 1, and the difference between the rear vibration parameter and the preset vibration parameter interval is 0.7, then the trend is the first trend, and the first trend is a similar trend. The difference between the front vibration parameter and the preset vibration parameter interval is 1, and the difference between the rear vibration parameter and the preset vibration parameter interval is 1, then the trend is the second trend, and the second trend is an equal trend. The difference between the front vibration parameter and the preset vibration parameter interval is 1, and the difference between the rear vibration parameter and the preset vibration parameter interval is 1.2, then the trend is the third trend, and the third trend is a distant trend.

[0126] In this embodiment, the first-level alarm signal is smaller than the second-level alarm signal, and the second-level alarm signal is smaller than the third-level alarm signal. According to the different levels of the alarm signal, operation and maintenance plans with different urgency levels are set to ensure safe operation, thereby reducing the maintenance costs and spare parts costs of the equipment, reducing the equipment operation and maintenance costs, and preventing serious production equipment accidents.

[0127] In some embodiments of the present application, a coal mill intelligent adjustment and control method is also included:

[0128] Step S201: setting characteristic components of the coal mill according to the historical vibration parameter set, and establishing multiple monitoring sub-areas according to the characteristic components;

[0129] Step S202: constructing a vibration-state evaluation model, and generating a state evaluation value for each monitoring sub-area according to the vibration-state prediction model;

[0130] Step S203: setting a monitoring time node of a corresponding monitoring sub-area according to the state evaluation value, collecting real-time vibration parameters of the corresponding component according to the monitoring time node and generating an abnormal probability of the corresponding monitoring sub-area;

[0131] Step S204: Determine the comprehensive operating state of the coal mill according to the abnormal probability of each monitoring sub-area, generate an adjustment instruction according to the comprehensive operating state, and determine whether to send an alarm signal.

[0132] In summary, the present invention discloses an intelligent adjustment control system and method for a coal mill, which system includes: a monitoring module, which is used to set the characteristic components of the coal mill according to the historical vibration parameter set, and establish multiple monitoring sub-areas according to the characteristic components; a construction module, which is used to construct a vibration-state evaluation model, and generate a state evaluation value of each monitoring sub-area according to the vibration-state prediction model; a prediction module, which is used to set the monitoring time node of the corresponding monitoring sub-area according to the state evaluation value, collect the real-time vibration parameters of the corresponding components according to the monitoring time node and generate the abnormal probability of the corresponding monitoring sub-area; an adjustment module, which is used to determine the comprehensive operation state of the coal mill according to the abnormal probability of each monitoring sub-area, generate an adjustment instruction according to the comprehensive operation state, and judge whether to send an alarm signal; by monitoring the vibration parameters of multiple characteristic components of the coal mill, determining the abnormal probability of each characteristic component and performing a comprehensive analysis, determining the comprehensive operation state of the coal mill, formulating reasonable adjustment instructions according to the comprehensive operation state, and judging whether to send an alarm signal according to the adjusted vibration parameters, the accuracy of the vibration monitoring data of the coal mill is improved, the equipment operation and maintenance costs are reduced, and the occurrence of vicious production equipment accidents is prevented.

[0133] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present application. These improvements and substitutions should also be regarded as the scope of protection of the present application.

Claims

1. A coal mill intelligent adjustment and control system, characterized in that: include: A monitoring module, used for setting characteristic components of the coal mill according to a historical vibration parameter set, and establishing a plurality of monitoring sub-areas according to the characteristic components; A construction module, used to construct a vibration-state evaluation model, and generate a state evaluation value of each monitoring sub-area according to the vibration-state prediction model; A prediction module is used to set a monitoring time node of a corresponding monitoring sub-area according to the state evaluation value, collect real-time vibration parameters of the corresponding component according to the monitoring time node, and generate an abnormal probability of the corresponding monitoring sub-area; An adjustment module is used to determine the comprehensive operation status of the coal mill according to the abnormal probability of each monitoring sub-area, generate an adjustment instruction according to the comprehensive operation status, and determine whether to send an alarm signal; The characteristic components of the coal mill are set according to the historical vibration parameter set, including: The historical vibration parameter set includes historical vibration parameters of several coal mill components, and the historical vibration parameters include historical vibration frequency, historical vibration amplitude and historical vibration phase; Generate a normal vibration parameter interval and an abnormal vibration parameter interval of a current component according to historical vibration parameters of the coal mill component; When the historical vibration parameter is in the abnormal vibration parameter interval, determining a first correlation between the current component and other components, and determining a second correlation between the current component and the coal mill operating state according to the historical adjustment instructions corresponding to the historical vibration parameter; Generate a comprehensive correlation m of the component according to the first correlation and the second correlation, and set the component whose comprehensive correlation is greater than a preset correlation threshold as a characteristic component of the coal mill; Generating a state evaluation value for each monitoring sub-area according to the vibration-state evaluation model includes: Divide the normal vibration parameter interval of the component into multiple normal vibration parameter sub-intervals according to the operating stage factors, and configure a state coefficient e for each normal vibration parameter sub-interval; Dividing the abnormal vibration parameter interval into a plurality of abnormal vibration parameter subintervals according to the divided time periods corresponding to the normal vibration parameter subintervals, and calculating a first critical difference amount and a second critical difference amount between the abnormal vibration parameter subinterval and the corresponding normal vibration parameter subinterval; Generate an initial state evaluation value A1 according to the first critical difference and the second critical difference, and generate a historical state evaluation value a of the historical vibration parameter of each component according to the initial state evaluation value A1, the state coefficient e and the comprehensive correlation m of the corresponding component; When the historical vibration parameter is in the abnormal vibration parameter sub-interval, a=A1*m, and when the historical vibration parameter is in the normal vibration parameter sub-interval, a=e*m; A vibration-state evaluation model is constructed based on historical vibration parameters and corresponding historical state evaluation values. The vibration parameters of each component in a preset period of time are input into the vibration-state evaluation model to generate a state evaluation value for the corresponding monitoring sub-area.

2. The coal mill intelligent adjustment and control system according to claim 1, characterized in that: The monitoring time nodes of the corresponding monitoring sub-areas are set according to the status evaluation values, including: According to the relationship between the state evaluation value and the preset state evaluation value interval, the time interval t between the current monitoring time node and the next monitoring time node of the monitoring sub-area is set; Presetting a first preset state evaluation value interval, a second preset state evaluation value interval, a third preset state evaluation value interval and a fourth preset state evaluation value interval; When the state evaluation value is in the first preset state evaluation value interval, the fourth preset time interval t4 is set as the time interval t between the current monitoring time node and the next monitoring time node, that is, t=t4; When the state evaluation value is in the second preset state evaluation value interval, the third preset time interval t3 is set as the time interval t between the current monitoring time node and the next monitoring time node, that is, t=t3; When the state evaluation value is in the third preset state evaluation value interval, the second preset time interval t2 is set as the time interval t between the current monitoring time node and the next monitoring time node, that is, t=t2; When the state evaluation value is in the fourth preset state evaluation value interval, the first preset time interval t1 is set as the time interval t between the current monitoring time node and the next monitoring time node, that is, t=t1; Among them, t1<t2<t3<t4.

3. The coal mill intelligent adjustment and control system according to claim 2, characterized in that: Generate the abnormal probability of the corresponding monitoring sub-area, including: Establish life prediction model for each component; Generate a life risk coefficient v for each component according to the life prediction model; Compare the real-time vibration parameters with the preset vibration parameter intervals of the corresponding components, and generate the initial abnormal probability p1 of each monitoring sub-area according to the comparison results; According to the life risk coefficient of each component and the initial abnormal probability of the corresponding monitoring sub-area, the abnormal probability P of each monitoring sub-area is generated, where P=p1*v.

4. The coal mill intelligent adjustment and control system according to claim 3, characterized in that: Determine the comprehensive operating status of the coal mill, including: Generate the comprehensive operation quantitative value K of the coal mill according to the abnormal probability P of each monitoring sub-area and the comprehensive correlation m of the corresponding components; Among them, Pi is the abnormal probability of the ith monitoring sub-area, and mi is the comprehensive correlation of the ith monitoring sub-area; Presetting a first preset operation quantization value interval, a second preset operation quantization value interval and a third preset operation quantization value interval; When the comprehensive operation quantization value K is in the first preset operation quantization value interval, it is determined that the comprehensive operation state of the coal mill is in a good state; When the comprehensive operation quantization value K is in the second preset operation quantization value interval, it is determined that the comprehensive operation state of the coal mill is a general state; When the comprehensive operation quantization value K is in the third preset operation quantization value interval, it is determined that the comprehensive operation state of the coal mill is an abnormal state.

5. The coal mill intelligent adjustment and control system according to claim 4, characterized in that: Generate adjustment instructions based on the comprehensive operating status, including: When the comprehensive operating status of the coal mill is in a good state, no adjustment instruction is generated; When the comprehensive operation state of the coal mill is a general state, a primary adjustment instruction is generated, wherein the primary adjustment instruction is to correct the operation load Y of the coal mill according to the first correction coefficient c1, and the corrected operation load is Y*c1; When the comprehensive operating state of the coal mill is abnormal, a secondary adjustment instruction is generated, which corrects the operating load Y of the coal mill according to the second correction coefficient c2. The corrected operating load is Y*c2, where c1>c2.

6. The coal mill intelligent adjustment and control system according to claim 5, characterized in that: Determine whether to send an alarm signal, including: Control the coal mill according to the adjustment instruction and obtain the adjusted vibration parameters of each monitoring sub-area after adjustment, compare the adjusted vibration parameters with the corresponding preset vibration parameter interval, and generate the trend of the adjusted vibration parameters; If the comprehensive operating state of the coal mill is a normal state and the trend of the adjusted vibration parameter is the first trend, no alarm signal is sent; If the comprehensive operating state of the coal mill is in a normal state and the trend of the adjusted vibration parameter is the second trend, a first-level alarm signal is sent; If the comprehensive operating state of the coal mill is in a normal state and the trend of the adjusted vibration parameter is the third trend, a secondary alarm signal is sent; If the comprehensive operating state of the coal mill is abnormal and the trend of the adjusted vibration parameter is the first trend, no alarm signal is sent; If the comprehensive operating state of the coal mill is abnormal and the trend of the adjusted vibration parameter is the second trend, a secondary alarm signal is sent; If the comprehensive operating state of the coal mill is abnormal and the trend of the adjusted vibration parameter is the third trend, a third-level alarm signal is sent and the mill is shut down; Among them, the first trend is a similar trend, the second trend is an equal trend, and the third trend is a distant trend.

7. A coal mill intelligent adjustment and control method, characterized in that: include: Set characteristic components of the coal mill according to the historical vibration parameter set, and establish multiple monitoring sub-areas according to the characteristic components; Constructing a vibration-state evaluation model, and generating a state evaluation value of each monitoring sub-area according to the vibration-state prediction model; According to the state evaluation value, a monitoring time node of the corresponding monitoring sub-area is set, and the real-time vibration parameters of the corresponding components are collected according to the monitoring time node to generate the abnormal probability of the corresponding monitoring sub-area; Determine the comprehensive operating status of the coal mill according to the abnormal probability of each monitoring sub-area, generate adjustment instructions according to the comprehensive operating status, and determine whether to send an alarm signal; The characteristic components of the coal mill are set according to the historical vibration parameter set, including: The historical vibration parameter set includes historical vibration parameters of several coal mill components, and the historical vibration parameters include historical vibration frequency, historical vibration amplitude and historical vibration phase; Generate a normal vibration parameter interval and an abnormal vibration parameter interval of a current component according to historical vibration parameters of the coal mill component; When the historical vibration parameter is in the abnormal vibration parameter interval, determining a first correlation between the current component and other components, and determining a second correlation between the current component and the coal mill operating state according to the historical adjustment instructions corresponding to the historical vibration parameter; Generate a comprehensive correlation m of the component according to the first correlation and the second correlation, and set the component whose comprehensive correlation is greater than a preset correlation threshold as a characteristic component of the coal mill; Generating a state evaluation value for each monitoring sub-area according to the vibration-state evaluation model includes: Divide the normal vibration parameter interval of the component into multiple normal vibration parameter sub-intervals according to the operating stage factors, and configure a state coefficient e for each normal vibration parameter sub-interval; Dividing the abnormal vibration parameter interval into a plurality of abnormal vibration parameter subintervals according to the divided time periods corresponding to the normal vibration parameter subintervals, and calculating a first critical difference amount and a second critical difference amount between the abnormal vibration parameter subinterval and the corresponding normal vibration parameter subinterval; Generate an initial state evaluation value A1 according to the first critical difference and the second critical difference, and generate a historical state evaluation value a of the historical vibration parameter of each component according to the initial state evaluation value A1, the state coefficient e and the comprehensive correlation m of the corresponding component; When the historical vibration parameter is in the abnormal vibration parameter sub-interval, a=A1*m, and when the historical vibration parameter is in the normal vibration parameter sub-interval, a=e*m; A vibration-state evaluation model is constructed based on historical vibration parameters and corresponding historical state evaluation values. The vibration parameters of each component in a preset period of time are input into the vibration-state evaluation model to generate a state evaluation value for the corresponding monitoring sub-area.

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