CN114626558A - Intelligent industrial production equipment management method and system - Google Patents
Intelligent industrial production equipment management method and system Download PDFInfo
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Abstract
The application relates to an intelligent industrial production equipment management method, which comprises the following steps: monitoring the running state of the equipment in real time, uploading the running state parameters of the equipment to a big data platform, forming an equipment running state parameter database, and generating a running state analysis model; recording the running state parameters of the equipment to be judged into a monitoring model, evaluating the running state of the equipment to obtain an evaluation result, and selecting corresponding measures according to the obtained evaluation result; and aiming at the running state parameter record and the equipment abnormal record of the equipment, combining an equipment production plan and making a maintenance plan. According to the method and the device, the running state parameters of the equipment are analyzed in detail from multiple angles, so that the evaluation capability of evaluating the running state of the equipment is improved; the long-term maintenance plan is formulated, so that the long-term stability of equipment operation is ensured, and the stability of the production process is improved; and the maintenance plan can be adjusted according to the change degree of the equipment operation parameters, so that the flexibility of production equipment management is improved.
Description
Technical Field
The application relates to the field of intelligent industry, in particular to a method and a system for managing intelligent industrial production equipment.
Background
CN201510636250.9 discloses a maintenance management system for industrial equipment based on big data, which has the technical solution: the system comprises a monitoring terminal arranged on each industrial device and a server end in wireless connection with the monitoring terminal; the monitoring terminal includes: the component working time monitoring module is used for monitoring the working time of the components on the corresponding industrial equipment; the first communication module is used for sending the monitored working time information of the parts corresponding to the industrial equipment to the server side, and the working time information at least carries the unique identifier of the monitoring terminal; the server side includes: the component service life recording module records the service life of the components on the industrial equipment; the part maintenance time recording module records the maintenance time period of each part, wherein the maintenance time period of each part is the service life of each part minus a corresponding preset time period; the second communication module is used for receiving the working time information sent by the first communication module; the accumulated working time counting module is used for counting the accumulated working time of the corresponding parts according to the working time received by the second communication module; the first judgment module is used for judging whether the accumulated working time of the parts belongs to the corresponding maintenance time period or not; the maintenance time determining module is used for determining the current accumulated working time as the maintenance time of the corresponding part when the first judging module judges that the accumulated working time belongs to the corresponding maintenance time period; the server side further comprises: the average service life counting module is corresponding to each type of parts and is used for counting the uniform average service life of the type according to the service lives of all the parts under the type; the component life recording module is also used for recording the average service life of each type of components; the component maintenance time recording module is further configured to obtain a maintenance time period of each component in each category according to the average service life of the component in the category, and record a maintenance time period corresponding to each category, where the maintenance time period of each component is obtained by subtracting a corresponding predetermined time period from the average service life of the category to which the component belongs.
The industrial equipment maintenance management system based on big data has the following advantages: related workers can maintain the industrial equipment when the industrial equipment is not in operation, and the normal working time of the industrial equipment is not occupied, so that each industrial equipment of the same production line can smoothly perform linkage or sequential action, the products of the production line are smoothly processed, the production efficiency of the products is greatly improved, and the yield is improved; on the other hand, various possible problems such as jamming, stopping, damage of the industrial equipment and the like of the industrial equipment can not occur due to damage of parts of the industrial equipment, so that the service life of the industrial equipment is greatly prolonged, and on the other hand, products cannot be damaged by the parts after the parts are damaged, so that the yield is greatly improved.
However, the industrial equipment maintenance management system based on big data also has the following disadvantages: only the working time and the service life of equipment and parts are considered, the reference quantity is single, and the difference between the actual service time and the service life is caused by being easily influenced by environmental factors and actual service factors; the maintenance plan of the equipment cannot be adjusted according to the actual use condition of the equipment, and the flexibility is poor.
Therefore, there is a need for a method or system that adjusts the time and schedule of maintenance of equipment based on a number of factors that are actually used by the equipment.
Disclosure of Invention
In order to solve the problems that the reference quantity is single and the equipment maintenance plan cannot be flexibly adjusted according to the actual use condition of the equipment, the application provides an intelligent industrial production equipment management method and system.
The application provides an intelligent industrial production equipment management method, which comprises the following steps:
step S1, monitoring the running state of the equipment, setting equipment numbers for different production equipment, monitoring the running state of the equipment in real time by an equipment monitoring device, uploading the running state parameters of the equipment to a big data platform, forming an equipment running state parameter database, and generating a running state analysis model;
step S2, judging the running state of the equipment, inputting the running state parameters of the equipment to be judged into a monitoring model, evaluating the running state of the equipment to obtain an evaluation result, and selecting corresponding measures according to the obtained evaluation result;
and step S3, making a long-term maintenance plan, and making a maintenance plan according to the running state parameter record and the equipment abnormal record of the equipment and the production plan of the equipment.
Further, the step S1 includes: step S11, generating equipment numbers, setting the equipment numbers for different production equipment, and uploading the equipment numbers to a big data platform; step S12, collecting parameters, collecting equipment running state parameters in real time by the equipment monitoring device, uploading the equipment running state parameters to a big data platform, and forming an equipment running state parameter database; step S13, generating an equipment curve, corresponding the equipment running state parameters to running time, and generating an equipment running state parameter-time curve; step S14, generating an operation state analysis model, analyzing the equipment operation state parameter curve, and combining the equipment fault record to obtain the rule between the equipment fault and each equipment operation state parameter to form an operation state analysis model; step S15, optimizing the model, continuously acquiring the running state parameters of the equipment, uploading the running state parameters to a big data platform, supplementing an equipment running state parameter database, and optimizing a running state analysis model;
in step S12, the device operating status parameters include, but are not limited to: equipment temperature, equipment operating sound, inlet or outlet gas flow.
Further, the step S14 includes: step S141, analyzing the special points, identifying sharp rising points or sharp falling points in the equipment operation state parameter curve, marking the sharp rising points or sharp falling points as the special points, and further analyzing the equipment and the operation time corresponding to the point; step S142, analyzing the peak and the valley of the curve, marking the peak and the valley of the curve, and analyzing equipment and running time corresponding to the appearance points of the peak and the valley; and S143, comparing and analyzing curves, namely comparing and analyzing curves in the reference curve database of the same equipment in the same mode, and evaluating the running state parameters of the equipment in the same mode.
By adopting the technical scheme, the running state parameters of the equipment are analyzed in detail respectively according to the change rate and the change trend of the running state parameters of the equipment and the difference between the running state parameters and the historical parameters, so that the evaluation capability of evaluating the running state of the equipment is improved.
Further, in step S141, the method for identifying the special point includes: and converging the slopes of all points of the equipment operation state parameter curve into a slope curve, and identifying the slope absolute value of the point with the slope absolute value exceeding 95% as a special point.
Further, in step S143, the curve comparison analysis method includes: comparing the parameter difference value of the corresponding time of the curve of the same mode in the reference curve database and the equipment running process to be evaluated to obtain the difference index of the equipment running state parameter of the equipment running process and the equipment running state parameter of the curve of the same mode in the reference curve database;
the calculation method of the difference index comprises the following steps:
wherein, the first and the second end of the pipe are connected with each other,
the difference index is expressed as the equipment running state parameter of a certain equipment running process and the equipment running state parameter of the curve with the same mode in the reference curve database;
the average value of the extreme differences of the parameter curve of the running state of the equipment running process and the parameter of a certain curve in the reference curve database at all the time points is represented;
the average value of the extreme differences of the operating state parameter curve of the equipment operating process and the parameters of a plurality of curves in the reference curve database at all the time is represented;
Xthe average value of the extreme difference of the parameters of the operation state parameter curve of the equipment operation process at all the time is expressed;
in step S143, the device operation state parameter curve whose difference index is smaller than the set threshold is recorded in the reference curve database, and the device operation state parameter curve whose difference index is larger than the set threshold is not recorded in the reference curve database.
By adopting the technical scheme, the difference index algorithm is introduced, the running state difference degree of the equipment running process and the running state difference degree of the historical running process are compared, the difference of the running states of the equipment is visually reflected, and the evaluation capability of the running states of the equipment is improved.
Further, the step S2 includes: step S21, evaluating the running state, and evaluating the running process of the equipment by combining the running state parameters of the equipment and the analysis result collected in the step S1: degree of difference index
If the difference index is smaller than the set threshold value, the running state of the equipment in the running process is qualified, and if the difference index is smaller than the set threshold value, the running state of the equipment in the running process is qualified
If the running state of the equipment is greater than the set threshold value, the running state of the equipment in the running process is unqualified; and step S22, taking corresponding measures for the equipment according to the operation state evaluation result, taking measures by referring to the equipment maintenance plan if the operation state of the equipment operation process of a certain time is qualified, and stopping the equipment and carrying out comprehensive inspection and maintenance on the equipment if the operation state of the equipment operation process of a certain time is unqualified.
Further, the step S3 includes: step S31, equipment checking plan, carrying out overall check on the equipment in fixed time, wherein the maximum equipment checking interval is determined by equipment abnormal record and equipment running state record, and the actual equipment checking time is adjusted according to the production plan but cannot exceed the maximum equipment checking interval; step S32, performing equipment maintenance on the equipment within a fixed time by an equipment maintenance plan, wherein the equipment maintenance plan includes but is not limited to adding scrubbing cleaning and oiling lubrication of the equipment, and the equipment maintenance time is consistent with the equipment inspection time; step S33, a part replacement plan is carried out, the used parts of the equipment are replaced within fixed time, and the replacement interval of the parts does not exceed the factory specified service life time of the parts;
the method for determining the equipment inspection period comprises the following steps:
wherein, T represents a device check period; t is expressed as all equipment is differentIn a normal situation, the median of the time difference of the abnormal occurrence time of two adjacent devices;
expressed as the mean value of the range of the slopes of all the special points of the curve and the corresponding moments of other curves in the same mode in the reference curve database;
expressed as the maximum value of the range of the slope of all the special points of the curve and the corresponding moments of other curves in the same mode in the reference curve database;
expressed as the mean value of the range of the parameters of all the peaks and the valleys of the curve and the corresponding moments of other curves in the same mode in the reference curve database;
the maximum value of the range of the parameter between all the peaks and the valley points of the curve and the corresponding time of other curves in the same mode in the reference curve database is shown.
By adopting the technical scheme, the long-term maintenance plan is made, so that the long-term stability of equipment operation is ensured, and the stability of the production process is improved; and the maintenance plan can be adjusted according to the change degree of the equipment operation parameters, so that the flexibility of production equipment management is improved.
An intelligent industrial production equipment management system comprising: the large data platform 1 and the equipment monitoring device 9;
the big data platform 1 comprises: a memory 2; a processor 3 connected to the memory 2; the equipment running state parameter database 4 is arranged in the memory 2 and is used for storing the original data of the equipment running state parameters; a reference curve database 5 for storing parameter curve data qualified by the evaluation of the equipment running state; the equipment abnormal record database 6 is used for storing abnormal record data of equipment faults;
the equipment monitoring device 9 is connected with the big data platform 1 and used for acquiring equipment running state parameters and transmitting data.
Further, the equipment monitoring device 9 includes: the device working temperature monitoring module 91 is used for acquiring the device working temperature in real time and uploading the device number corresponding to the device working temperature to the big data platform 1; the device working time monitoring module 92 is configured to monitor a starting point and an ending point of device working, calculate device working time, and upload a device number corresponding to the device working time to the big data platform 1; the device operation sound monitoring module 93 is used for acquiring decibel values and frequencies of sounds generated by device operation and uploading device operation sound parameters corresponding to device numbers to the big data platform 1; the part running time monitoring module 94 is used for monitoring the starting point and the ending point of the part operation, calculating the running time of the part starting, and uploading the part running time to the big data platform 1; and the gas flow meter 95 is used for acquiring the flow of gas introduced or discharged by the equipment and uploading the gas flow data to the big data platform 1.
Further, the method also comprises the following steps: the equipment running state analysis module 7 runs on the processor 3, is connected with the equipment abnormal record database 6, and is used for analyzing the equipment running state parameters, identifying special points and peak-valley points of a parameter curve, and obtaining an equipment maintenance plan by combining the equipment abnormal record database 6; and the equipment running state evaluation module 8 runs on the processor 3, is connected with the reference curve database 5, and is used for evaluating whether the equipment running state is qualified or not and transmitting the parameters of the qualified equipment running state to the reference curve database 5.
To sum up, the application comprises the following beneficial technical effects:
1. by analyzing the equipment running state parameters in detail from multiple angles, the evaluation capability of evaluating the running state of the equipment is improved;
2. the long-term maintenance plan is formulated, so that the long-term stability of equipment operation is ensured, and the stability of the production process is improved; the maintenance plan can be adjusted according to the change degree of the equipment operation parameters, so that the flexibility of production equipment management is improved;
3. by introducing a difference index algorithm, the difference degree of the running states of the equipment running process and the historical running process is compared, the difference of the running states of the equipment is visually reflected, and the evaluation capability of the running states of the equipment is improved.
Drawings
Fig. 1 is a step diagram of a method for managing an intelligent industrial production facility according to an embodiment of the present application.
Fig. 2 is a structural diagram of an intelligent industrial production equipment management system according to an embodiment of the present application.
Description of reference numerals:
1. a big data platform; 2. a memory; 3. a processor; 4. a device operating state parameter database; 5. a reference curve database; 6. an equipment exception record database; 7. an equipment running state analysis module; 8. an equipment running state evaluation module;
9. a device monitoring apparatus; 91. the device working temperature monitoring module; 92. a device working time monitoring module; 93. the equipment operation sound monitoring module; 94. a part operation time monitoring module; 95. a gas flow meter.
Detailed Description
The following description of the embodiments with reference to the drawings is provided to describe the embodiments, and the embodiments of the present application, such as the shapes and configurations of the components, the mutual positions and connection relationships of the components, the functions and working principles of the components, the manufacturing processes and the operation and use methods, etc., will be further described in detail to help those skilled in the art to more fully, accurately and deeply understand the inventive concepts and technical solutions of the present invention. For convenience of description, the directions mentioned in the present application shall be those shown in the drawings.
Referring to fig. 1-2, a method for managing intelligent industrial production equipment includes the following steps:
step S1, monitoring the running state of the equipment, setting equipment numbers for different production equipment, monitoring the running state of the equipment in real time by the equipment monitoring device 9, uploading the running state parameters of the equipment to the big data platform 1, forming an equipment running state parameter database 4, and generating a running state analysis model;
step S2, judging the running state of the equipment, inputting the running state parameters of the equipment to be judged into a monitoring model, evaluating the running state of the equipment to obtain an evaluation result, and selecting corresponding measures according to the obtained evaluation result;
and step S3, making a long-term maintenance plan, and making a maintenance plan according to the running state parameter record and the equipment abnormal record of the equipment and the production plan of the equipment.
The step S1 includes: step S11, generating equipment numbers, setting the equipment numbers for different production equipment, and uploading the equipment numbers to the big data platform 1; step S12, collecting parameters, collecting equipment running state parameters in real time by the equipment monitoring device 9, uploading the equipment running state parameters to the big data platform 1, and forming an equipment running state parameter database 4; step S13, generating an equipment curve, corresponding the equipment running state parameter to the running time, and generating an equipment running state parameter-time curve; step S14, generating an operation state analysis model, analyzing the equipment operation state parameter curve, and combining the equipment fault record to obtain the rule between the equipment fault and each equipment operation state parameter to form an operation state analysis model; step S15, optimizing the model, continuously acquiring the running state parameters of the equipment, uploading the running state parameters to the big data platform 1, supplementing the running state parameter database 4 of the equipment, and optimizing the running state analysis model;
in step S12, the device operating status parameters include, but are not limited to: equipment temperature, equipment operating sound, and gas flow rate of inlet or outlet.
The step S14 includes: step S141, analyzing the special points, identifying sharp rising points or sharp falling points in the equipment operation state parameter curve, marking the sharp rising points or sharp falling points as the special points, and further analyzing the equipment and the operation time corresponding to the point; step S142, analyzing the peak and the valley of the curve, marking the peak and the valley of the curve, and analyzing equipment and running time corresponding to the appearance points of the peak and the valley; and step S143, comparing and analyzing curves, namely comparing and analyzing the curves in the reference curve database 5 of the same device in the same mode, and evaluating the device running state parameters of the device in the same mode.
In step S141, the method for identifying the special point includes: and converging the slopes of all points of the equipment operation state parameter curve into a slope curve, and identifying the slope absolute value of the point with the slope absolute value exceeding 95% as a special point.
In step S143, the curve comparison analysis method includes: comparing the parameter difference value of the corresponding time of the curve of the same mode in the reference curve database 5 and the equipment running process to be evaluated to obtain the difference index of the equipment running state parameter of the equipment running process and the equipment running state parameter of the curve of the same mode in the reference curve database 5;
the calculation method of the difference index comprises the following steps:
wherein, the first and the second end of the pipe are connected with each other,
the difference index of the equipment running state parameter of the equipment running process and the equipment running state parameter of the curve with the same mode in the reference curve database 5 is expressed;
the average value of the extreme difference between the operating state parameter curve of the equipment operating process and the parameter of a certain curve in the reference curve database 5 at all the time points is represented;
the average value of the extreme difference between the operation state parameter curve of the equipment operation process and the parameters of the plurality of curves in the reference curve database 5 at all the time points is represented;
Xthe average value of the extreme difference of the parameters of the operation state parameter curve of the equipment operation process at all the time is expressed;
in step S143, the device operation state parameter curve whose difference index is smaller than the set threshold is recorded in the reference curve database 5, and the device operation state parameter curve whose difference index is greater than the set threshold is not recorded in the reference curve database 5.
The step S2 includes: step S21, evaluating the running state, and evaluating the running process of the equipment by combining the running state parameters of the equipment and the analysis result collected in the step S1: degree of difference index
If the difference index is smaller than the set threshold value, the running state of the equipment in the running process is qualified, and if the difference index is smaller than the set threshold value, the running state of the equipment in the running process is qualified
If the running state of the equipment is greater than the set threshold value, the running state of the equipment in the running process is unqualified; and step S22, taking corresponding measures for the equipment according to the operation state evaluation result, taking measures by referring to the equipment maintenance plan if the operation state of the equipment operation process of a certain time is qualified, and stopping the equipment and carrying out comprehensive inspection and maintenance on the equipment if the operation state of the equipment operation process of a certain time is unqualified.
The step S3 includes: step S31, equipment checking plan, carrying out overall check on the equipment in fixed time, wherein the maximum equipment checking interval is determined by equipment abnormal record and equipment running state record, and the actual equipment checking time is adjusted according to the production plan but cannot exceed the maximum equipment checking interval; step S32, performing equipment maintenance on the equipment within a fixed time by an equipment maintenance plan, wherein the equipment maintenance plan includes but is not limited to adding scrubbing cleaning and oiling lubrication of the equipment, and the equipment maintenance time is consistent with the equipment inspection time; step S33, a part replacement plan is carried out, the used parts of the equipment are replaced within a fixed time, and the replacement interval of the parts does not exceed the specified service life time of the parts leaving factory;
the method for determining the equipment inspection period comprises the following steps:
wherein, T represents a device check period; t represents all equipment abnormal conditionsThe median of the time difference between the two adjacent abnormal equipment occurrence times;
expressed as the mean of the range of slopes of all the special points of the curve and the corresponding moments of the other curves in the same mode in the reference curve database 5;
expressed as the maximum value of the range of the slope of all the special points of the curve with respect to the corresponding time of the other curves in the same mode in the reference curve database 5;
expressed as the mean value of the range of the parameters of all the peaks and the valleys of the curve and the corresponding moments of other curves in the same mode in the reference curve database 5;
expressed as the maximum value of the range of the parameter between all the peaks and valleys of the curve and the corresponding time points of the other curves in the same mode in the reference curve database 5.
An intelligent industrial production equipment management system comprising: the large data platform 1 and the equipment monitoring device 9;
the big data platform 1 comprises: a memory 2; a processor 3 connected to the memory 2; the equipment running state parameter database 4 is arranged in the memory 2 and is used for storing the original data of the equipment running state parameters; a reference curve database 5 for storing parameter curve data qualified by the evaluation of the equipment running state; the equipment abnormal record database 6 is used for storing abnormal record data of equipment faults;
the equipment monitoring device 9 is connected with the big data platform 1 and used for acquiring equipment running state parameters and transmitting data.
The device monitoring apparatus 9 includes: the device working temperature monitoring module 91 is used for acquiring the device working temperature in real time and uploading the device number corresponding to the device working temperature to the big data platform 1; the device working time monitoring module 92 is configured to monitor a starting point and an ending point of device working, calculate device working time, and upload a device number corresponding to the device working time to the big data platform 1; the device operation sound monitoring module 93 is used for acquiring decibel values and frequencies of sounds generated by device operation and uploading device numbers corresponding to device operation sound parameters to the big data platform 1; the part running time monitoring module 94 is used for monitoring the starting point and the ending point of the part operation, calculating the running time of the part starting, and uploading the part running time to the big data platform 1; and the gas flow meter 95 is used for acquiring the gas flow introduced into or discharged from the equipment and uploading the gas flow data to the big data platform 1.
Further comprising: the equipment running state analysis module 7 runs on the processor 3, is connected with the equipment abnormity record database 6, and is used for analyzing the equipment running state parameters, identifying special points and peak-valley points of a parameter curve, and obtaining an equipment maintenance plan by combining the equipment abnormity record database 6; and the equipment running state evaluation module 8 runs on the processor 3, is connected with the reference curve database 5, and is used for evaluating whether the equipment running state is qualified or not and transmitting the parameters of the qualified equipment running state to the reference curve database 5.
The working principle of the intelligent industrial production equipment management method and system is as follows: the running state parameters of the equipment are analyzed in detail respectively according to the change rate and the change trend of the running state parameters of the equipment and the difference between the running state parameters and the historical parameters, so that the evaluation capability of evaluating the running state of the equipment is improved; a long-term maintenance plan is made, so that the long-term stability of equipment operation is ensured, and the stability of the production process is improved; and the maintenance plan can be adjusted according to the change degree of the equipment operation parameters, so that the flexibility of production equipment management is improved.
In the embodiment of the application, the running state difference degree of the equipment running process and the running state difference degree of the historical running process are compared by introducing the difference index algorithm, so that the difference of the running states of the equipment is visually reflected, and the evaluation capability of the running states of the equipment is improved.
The present invention and its embodiments have been described above in an illustrative manner, and the description is not intended to be limiting, and the drawings are only one embodiment of the present invention, and the actual structure is not limited thereto. Therefore, if the person skilled in the art receives the teaching, the technical scheme and the embodiments similar to the technical scheme are not creatively designed without departing from the spirit of the invention, and the invention shall fall into the protection scope of the invention.
Claims (10)
1. An intelligent industrial production equipment management method is characterized by comprising the following steps:
step S1, monitoring the running state of the equipment, setting equipment numbers for different production equipment, monitoring the running state of the equipment in real time by an equipment monitoring device (9), uploading the running state parameters of the equipment to a big data platform (1), forming an equipment running state parameter database (4), and generating a running state analysis model;
step S2, judging the running state of the equipment, inputting the running state parameters of the equipment to be judged into a monitoring model, evaluating the running state of the equipment to obtain an evaluation result, and selecting corresponding measures according to the evaluation result;
and step S3, making a long-term maintenance plan, and making a maintenance plan according to the running state parameter record and the equipment abnormal record of the equipment and the production plan of the equipment.
2. The intelligent industrial production equipment management method according to claim 1, characterized in that:
the step S1 includes: step S11, generating equipment numbers, setting the equipment numbers for different production equipment, and uploading the equipment numbers to the big data platform (1); step S12, collecting parameters, collecting equipment running state parameters in real time by an equipment monitoring device (9), uploading the equipment running state parameters to a big data platform (1), and forming an equipment running state parameter database (4); step S13, generating an equipment curve, corresponding the equipment running state parameter to the running time, and generating an equipment running state parameter-time curve; step S14, generating an operation state analysis model, analyzing the equipment operation state parameter curve, and combining the equipment fault record to obtain the rule between the equipment fault and each equipment operation state parameter to form an operation state analysis model; step S15, optimizing the model, continuously acquiring the running state parameters of the equipment, uploading the running state parameters to a big data platform (1), supplementing an equipment running state parameter database (4), and optimizing a running state analysis model;
in step S12, the device operating status parameters include, but are not limited to: equipment temperature, equipment operating sound, inlet or outlet gas flow.
3. The intelligent industrial production equipment management method according to claim 1, characterized in that:
the step S14 includes: step S141, analyzing the special points, identifying sharp rising points or sharp falling points in the equipment running state parameter curve, and further analyzing equipment and running time corresponding to the points after marking the points as the special points; step S142, analyzing the peak and the valley of the curve, marking the peak and the valley of the curve, and analyzing equipment and running time corresponding to the appearance points of the peak and the valley; and S143, comparing and analyzing curves, namely comparing and analyzing curves in the reference curve database (5) of the same equipment in the same mode, and evaluating the running state parameters of the equipment in the same mode.
4. The intelligent industrial production equipment management method according to claim 3, characterized in that:
in step S141, the method for identifying the special point includes: and converging the slopes of all points of the equipment operation state parameter curve into a slope curve, and identifying the slope absolute value of the point with the slope absolute value exceeding 95% as a special point.
5. The intelligent industrial production equipment management method according to claim 3, characterized in that:
in step S143, the curve comparison analysis method includes: comparing the parameter difference value of the equipment running process to be evaluated with the same-mode curve in the reference curve database (5) at the corresponding moment to obtain the difference index of the equipment running state parameter of the equipment running process and the equipment running state parameter of the same-mode curve in the reference curve database (5);
the calculation method of the difference index comprises the following steps:
wherein the content of the first and second substances,
the difference index of the equipment running state parameter of the equipment running process and the equipment running state parameter of the curve with the same mode in the reference curve database (5) is expressed;
the average value of the extreme difference of the parameter curve of the running state of the equipment running process and the parameter of a certain curve in the reference curve database (5) at all the time points is expressed;
the average value of the operating state parameter curve of the equipment operating process and the extreme difference of the parameters of a plurality of curves in the reference curve database (5) at all the time is expressed;
Xthe average value of the extreme difference of the parameters of the operation state parameter curve of the equipment operation process at all the time is expressed;
in the step S143, the device operation state parameter curve whose difference index is smaller than the set threshold is recorded in the reference curve database (5), and the device operation state parameter curve whose difference index is larger than the set threshold is not recorded in the reference curve database (5).
6. The intelligent industrial production equipment management method according to claim 5, characterized in that:
the step S2 includes: step S21, evaluating the running state, and evaluating the running process of the equipment by combining the running state parameters of the equipment and the analysis result collected in the step S1: degree of difference index
If the difference index is smaller than the set threshold value, the running state of the equipment in the running process is qualified, and if the difference index is smaller than the set threshold value, the running state of the equipment in the running process is qualified
If the running state of the equipment is greater than the set threshold value, the running state of the equipment in the running process is unqualified; and step S22, taking corresponding measures for the equipment according to the operation state evaluation result, taking measures by referring to the equipment maintenance plan if the operation state of the equipment operation process of a certain time is qualified, and stopping the equipment and carrying out comprehensive inspection and maintenance on the equipment if the operation state of the equipment operation process of a certain time is unqualified.
7. The intelligent industrial production equipment management method according to claim 6, characterized in that:
the step S3 includes: step S31, equipment checking plan, carrying out overall check on the equipment in fixed time, wherein the maximum equipment checking interval is determined by equipment abnormal record and equipment running state record, and the actual equipment checking time is adjusted according to the production plan but cannot exceed the maximum equipment checking interval; step S32, performing equipment maintenance on the equipment within a fixed time by an equipment maintenance plan, wherein the equipment maintenance plan includes but is not limited to adding scrubbing cleaning and oiling lubrication of the equipment, and the equipment maintenance time is consistent with the equipment inspection time; step S33, a part replacement plan is carried out, the used parts of the equipment are replaced within a fixed time, and the replacement interval of the parts does not exceed the specified service life time of the parts leaving factory;
the method for determining the equipment inspection period comprises the following steps:
wherein, T represents a device check period; t is expressed as the median of the time difference of the abnormal occurrence time of the two adjacent devices in all the abnormal conditions of the devices;
expressed as the mean value of the range of the slopes of all the special points of the curve and the corresponding moments of other curves in the same mode in the reference curve database (5);
expressed as the maximum value of the range of the slope of all the special points of the curve and the corresponding moments of other curves in the same mode in the reference curve database (5);
expressed as the mean value of the range of the parameters of all the peaks and the valleys of the curve and the corresponding moments of other curves in the same mode in the reference curve database (5);
the maximum value of the range of the parameter between all the peaks and the valley points of the curve and the corresponding time of other curves in the same mode in the reference curve database (5) is shown.
8. An intelligent industrial production facility management system characterized by being adapted to the intelligent industrial production facility management method of any one of claims 1 to 7:
the intelligent industrial production equipment management system comprises: a big data platform (1) and a device monitoring device (9);
the big data platform (1) comprises: a memory (2); a processor (3) connected to the memory (2); the equipment running state parameter database (4) is arranged in the memory (2) and is used for storing the original data of the equipment running state parameters; the reference curve database (5) is used for storing parameter curve data qualified by the evaluation of the equipment running state; the equipment abnormity record database (6) is used for storing abnormity record data of equipment faults;
the equipment monitoring device (9) is connected with the big data platform (1) and is used for collecting equipment running state parameters and transmitting data.
9. The intelligent industrial production equipment management system of claim 8, wherein:
the device monitoring apparatus (9) includes: the device working temperature monitoring module (91) is used for acquiring the working temperature of the device in real time and uploading the device number corresponding to the working temperature of the device to the big data platform (1); the device working time monitoring module (92) is used for monitoring the starting point and the ending point of the device working, calculating the device working time and uploading the device number corresponding to the device working time to the big data platform (1); the equipment operation sound monitoring module (93) is used for acquiring decibel values and frequencies of sounds generated by equipment in operation and uploading equipment numbers corresponding to equipment operation sound parameters to the big data platform (1); the part running time monitoring module (94) is used for monitoring the starting point and the ending point of the part operation, calculating the starting running time of the part and uploading the part running time to the big data platform (1); and the gas flow meter (95) is used for acquiring the gas flow of the gas introduced into or discharged from the equipment and uploading the gas flow data to the big data platform (1).
10. The intelligent industrial production equipment management system of claim 9, wherein:
further comprising: the equipment running state analysis module (7) runs on the processor (3), is connected with the equipment abnormal record database (6), and is used for analyzing the equipment running state parameters, identifying special points and peak-valley points of a parameter curve, and obtaining an equipment maintenance plan by combining the equipment abnormal record database (6); and the equipment running state evaluation module (8) runs on the processor (3), is connected with the reference curve database (5) and is used for evaluating whether the equipment running state is qualified or not and transmitting the parameters of the qualified equipment running state to the reference curve database (5).
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| CN115239216B (en) * | 2022-09-23 | 2023-03-24 | 深圳市微优微科技有限公司 | Method, device, equipment and storage medium for preventive planned maintenance of production resources |
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