A multi-type status monitoring and early warning method for machine operation monitoring

Abstract

The invention discloses a multi-type state monitoring and early warning method for machine operation monitoring, and specifically relates to the technical field of FPC board processing. The invention uses a temperature sensor or a pressure sensor to monitor the real-time temperature or real-time pressure of a machine whose temperature or pressure needs to be controlled during the FPC board processing process, uses a speed sensor to monitor the conveyor belt speed and the cutting speed, and uses a camera to record the motion trajectory and the operation point position of the machine whose positioning accuracy needs to be controlled, thereby expanding the monitoring range, being beneficial to improving the comprehensiveness of abnormality identification, and being beneficial to improving the accuracy of abnormality identification. When the real-time temperature or real-time pressure is in an allowable temperature fluctuation range or an allowable pressure fluctuation range, the invention judges whether the real-time temperature or real-time pressure presents a sudden change, a decreasing state or an increasing state, and regulates the real-time temperature and pressure based on the judgment result, which is beneficial to timely regulation of machine parameters and reducing the abnormal operation time of the machine.

Description

Technical Field

[0001] The present invention relates to the technical field of FPC board processing, and more specifically, to a multi-type status monitoring and early warning method for machine operation monitoring. Background Art

[0002] In the existing FPC board processing process, the stable operation of the machine is a key factor in ensuring product quality and production efficiency. As a result, many monitoring methods for machine operation monitoring have emerged.

[0003] The existing FPC board processing machine operation monitoring method uses temperature sensors and pressure sensors to monitor the temperature data and pressure data during the operation of the machine, and sends abnormal operation warnings to the management center when the temperature data and pressure data exceed the set thresholds. This realizes machine operation monitoring to a certain extent and reduces the economic losses and frequency of dangerous events caused by abnormal machine operation.

[0004] However, the above system still has some problems: the above system only monitors temperature and pressure, and the monitoring range is limited. It cannot fully cover all key parameters of machine operation. It only sends abnormal operation warnings when the temperature and pressure exceed the threshold. The ability to identify and warn of abnormal machine conditions is insufficient. It can often only be dealt with after the problem occurs. It lacks initiative, has weak data processing and analysis capabilities, and cannot effectively extract useful information from a large amount of monitoring data, resulting in low accuracy of warnings. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a multi-type status monitoring and early warning method for machine operation monitoring to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a multi-type status monitoring and early warning method for machine operation monitoring, comprising the following steps:

[0007] S1: Set the temperature fluctuation range or pressure fluctuation range for the machine that needs to control the temperature or pressure during the FPC board processing, set the conveyor speed and cutting speed, and set the motion trajectory, operation point position and operation point position allowable error for the machine that needs to control the positioning accuracy;

[0008] S2: For machines that need to control temperature or pressure during the FPC board processing process, use temperature sensors or pressure sensors to monitor the real-time temperature or real-time pressure of the machine, use speed sensors to monitor the conveyor belt speed and cutting speed, and use cameras to record the movement trajectory and operation point position of the machine that needs to control positioning accuracy;

[0009] S3: Analyze the monitored data to determine whether the machine parameters need to be adjusted or whether there is any abnormality in the machine;

[0010] S4: When it is determined that the machine parameters need to be adjusted, the machine enters the automatic adjustment mode, sends the adjustment object, time, and data before and after the adjustment to the management center, and if the automatic adjustment fails, the machine enters the manual adjustment mode, and the management center performs manual adjustment. If the manual adjustment fails, the machine parameter adjustment is determined to be abnormal;

[0011] S5: When it is determined that there is an abnormality in the machine or the machine adjustment is abnormal, the process flow corresponding to the machine will be suspended, and the machine abnormality or the machine parameter adjustment process abnormality will be identified through the trained abnormality recognition model, and the abnormality recognition result will be sent to the management center;

[0012] S6: Arrange maintenance personnel to perform abnormal maintenance based on the abnormal identification results, record the number of machines undergoing abnormal maintenance, the number of machines undergoing normal operation, and the number of abnormal maintenance during each FPC board processing task cycle, and calculate the machine availability coefficient and abnormal occurrence rate during each abnormal maintenance;

[0013] S7: Record the number of machine operation point position errors, the number of machine operation point correct positions, and the operation point position error each time the machine operation point position is wrong within the FPC board processing task cycle to calculate the positioning accuracy control capability coefficient, calculate the theoretical maximum number of FPC board finished products based on the materials used for FPC board processing, and calculate the yield rate based on the actual number of FPC board finished products and the theoretical maximum number of FPC board finished products;

[0014] S8: Calculate the average machine availability coefficient based on the average machine availability coefficient, abnormality occurrence rate, positioning accuracy control capability coefficient, and yield rate computer operation quality comprehensive evaluation index within the FPC board processing task cycle.

[0015] Preferably, the specific steps of data analysis in step S3 are as follows:

[0016] S31. When the real-time temperature is in the temperature fluctuation range allowed, determine whether the real-time temperature shows a sudden change, a decreasing or an increasing state. If it shows a sudden change, immediately adjust the machine temperature. If it shows a decreasing or increasing state, calculate the temperature decreasing coefficient or the temperature increasing coefficient and then predict the time required for the real-time temperature to reach the critical value of the temperature fluctuation range allowed. Compare it with the preset adjustment reaction time. If the predicted value is greater than the preset value, do not adjust it temporarily. If the predicted value is less than or equal to the preset value, immediately adjust the machine temperature.

[0017] S32. When the real-time pressure is in the pressure fluctuation range, determine whether the real-time pressure shows a sudden change, a decreasing or an increasing state. If it shows a sudden change, the machine pressure is immediately regulated. If it shows a decreasing or increasing state, the pressure decreasing coefficient or the pressure increasing coefficient is calculated to predict the time required for the real-time pressure to reach the critical value of the pressure fluctuation range, and the time is compared with the preset adjustment reaction time. If the predicted value is greater than the preset value, no regulation is performed temporarily. If the predicted value is less than or equal to the preset value, the machine pressure is immediately regulated.

[0018] S33, when the real-time temperature or real-time pressure exceeds the set temperature allowable fluctuation range or pressure allowable fluctuation range, it is determined that the machine is abnormal;

[0019] S34. Compare the real-time conveyor belt speed, cutting speed, machine motion trajectory, and operation point position with the set parameters. If the real-time conveyor belt speed, cutting speed, and machine motion trajectory are inconsistent with the set values, it is determined that they need to be adjusted. If the error between the real-time operation point position and the set operation point position is less than or equal to the set operation point position allowable error, there is no need to adjust the operation point. If the error between the real-time operation point position and the set operation point position is greater than the set operation point position allowable error, the operation point needs to be adjusted.

[0020] Preferably, the specific state judgment process for the real-time temperature within the allowable temperature fluctuation range in step S31 is as follows:

[0021] S311, use T aij It represents the temperature of the i-th machine that needs to control the temperature at the j-th moment. When the temperature of the i-th machine that needs to control the temperature suddenly changes at the j+1th moment, the temperature of the machine needs to be adjusted immediately, σ Ti is the preset temperature mutation coefficient of the i-th machine that needs to control the temperature. It is determined that the temperature of the i-th machine that needs to control the temperature has not changed suddenly at the j+1th moment;

[0022] S312: If the real-time temperature does not suddenly change, determine whether the real-time temperature is decreasing or increasing. or When the real-time temperature decreases or increases, T aij+2 For the temperature of the i-th machine that needs to control the temperature at the j+2th moment, calculate the temperature reduction coefficient δ Ta Or the temperature increase coefficient δ Tb , the specific formula is as follows: , ;

[0023] S313: the predicted time t of the real-time temperature of the i-th machine that needs to control the temperature to reach the critical value of the temperature fluctuation range when the real-time temperature decreases ai The calculation formula is: , T bimin is the minimum allowable temperature of the i-th machine that needs to control the temperature, and the predicted time t for the real-time temperature of the i-th machine that needs to control the temperature to reach the critical value of the temperature fluctuation range when the real-time temperature increases. bi The calculation formula is: , T bimax is the maximum allowable temperature of the i-th machine that needs to control temperature;

[0024] S314, will t ai or bi Adjust the reaction time t yTi For comparison, or The machine temperature is temporarily not regulated, otherwise the machine temperature is immediately regulated. If the subsequent real-time temperature still decreases or increases when the machine temperature is not regulated, the temperature decrease coefficient or temperature increase coefficient and subsequent prediction and judgment results are updated in real time.

[0025] Preferably, the specific state judgment process for the real-time pressure within the pressure fluctuation range in step S32 is as follows:

[0026] S321, use P aij It represents the pressure of the i-th machine that needs to control the pressure at the j-th moment. When the pressure of the i-th machine that needs to control the pressure suddenly changes at the j+1th moment, the pressure of the machine needs to be adjusted immediately, σ Pi is the preset pressure mutation coefficient of the i-th machine that needs to control the pressure. It is determined that the pressure of the i-th machine that needs to control the pressure has not changed suddenly at the j+1th moment;

[0027] S322: If the real-time pressure does not suddenly change, determine whether the real-time pressure is decreasing or increasing. or When judging whether the real-time pressure decreases or increases, P aij+2 For the pressure of the i-th machine that needs to control the temperature at the j+2th moment, calculate the pressure reduction coefficient δ Pa Or pressure increase coefficient δ Pb , the specific formula is as follows: , ;

[0028] S323, the predicted time t of the real-time pressure of the i-th machine that needs to control the temperature to reach the critical value of the pressure fluctuation range when the real-time pressure decreasesci The calculation formula is: , P bimin is the minimum allowable pressure of the i-th machine that needs to control pressure, and the predicted time t for the real-time pressure of the i-th machine that needs to control temperature to reach the critical value of the pressure allowable fluctuation range when the real-time pressure increases di The calculation formula is: , P bimax is the maximum allowable pressure of the i-th machine that needs to control pressure;

[0029] S324, will t ci or di Adjust the reaction time t yPi For comparison, or The machine pressure will not be regulated temporarily, otherwise it will be regulated immediately. If the subsequent real-time pressure still decreases or increases when the machine pressure is not regulated, the pressure decrease coefficient or pressure increase coefficient and subsequent prediction and judgment results will be updated in real time.

[0030] Preferably, the specific data processing process in step S6 is as follows:

[0031] S61, record the FPC board processing task cycle t z The number of machines undergoing abnormal maintenance within the i-th period m ywi 、Number of machines in normal operation m zyi And the number of abnormal maintenance n yw ;

[0032] S62, the number of machines m that underwent abnormal maintenance for the i-th time during the FPC board processing task cycle ywi and the number of machines in normal operation m zyi Calculate the machine availability rate X during the i-th abnormal maintenance ki , the specific formula is: ;

[0033] S63, based on the FPC board processing task cycle t z And the number of abnormal maintenance times n during the FPC board processing task cycle yw Calculate the abnormality rate X f , the specific formula is: .

[0034] Preferably, the specific data processing process in step S7 is as follows:

[0035] S71, record the number of machine operation point position errors m during the FPC board processing task cycle wc 、Number of correct machine operation point positions m wz , the operating point position error ε when the i-th machine operating point has the j-th position error aij, the position allowable error of the i-th machine operating point ε bi 、The theoretical maximum number of finished FPC boards m calculated based on the materials used in FPC board processing cl , and the actual number of finished FPC boards m cs ;

[0036] S72, based on the number of position errors n of the i-th machine operation point in the FPC board processing task cycle wci , the number of correct positions of the i-th machine operating point n wzi , the operating point position error ε when the i-th machine operating point has the j-th position error aij 、The position allowable error εbi of the i-th machine operation point is used to calculate the positioning accuracy control capability coefficient X dc , the specific formula is: , nc is the number of machine operation points;

[0037] S73, based on the theoretical maximum number of finished FPC boards mcl and the actual number of finished FPC boards m cs Calculate the yield rate X cp , the specific formula is: .

[0038] Preferably, the specific data processing process in step S8 is as follows:

[0039] S81, the calculated machine availability rate X during the i-th abnormal maintenance ki After integration, calculate the average machine availability factor X ke , the specific formula is: ;

[0040] S82, based on the average machine availability coefficient, abnormality occurrence rate, positioning accuracy control capability coefficient, and yield rate within the FPC board processing task cycle, the comprehensive evaluation index F of the machine operation quality YZ , the specific calculation formula is: .

[0041] Technical effects and advantages of the present invention:

[0042] 1. The present invention uses a temperature sensor or a pressure sensor to monitor the real-time temperature or real-time pressure of the machine that needs to control the temperature or pressure during the FPC board processing process, uses a speed sensor to monitor the conveyor belt speed and the cutting speed, and uses a camera to record the motion trajectory and the operation point position of the machine that needs to control the positioning accuracy, thereby expanding the monitoring range and being beneficial to improving the comprehensiveness of abnormality identification; when the present invention determines that the machine has an abnormality or the machine adjustment is abnormal, the corresponding process flow of the machine will be suspended, and the machine abnormality or the machine parameter adjustment process abnormality will be identified through a trained abnormality recognition model, so that the cause of the abnormality can be quickly identified, the subsequent abnormal maintenance efficiency can be improved, and the abnormal maintenance downtime of the machine can be reduced; the present invention calculates the average machine availability coefficient, and calculates the comprehensive evaluation index of the machine operation quality based on the average machine availability coefficient, the abnormality occurrence rate, the positioning accuracy control capability coefficient, and the yield rate within the FPC board processing task cycle, thereby providing a method for evaluating the machine operation quality, intuitively displaying the machine operation quality, and providing data support for subsequent FPC board processing process optimization and machine update.

[0043] 2. When the real-time temperature is in the temperature allowable fluctuation range, the present invention determines whether the real-time temperature presents a sudden change, a decreasing or an increasing state. If a sudden change is present, the machine temperature is immediately regulated. If a decrease or an increase is present, the temperature decrease coefficient or the temperature increase coefficient is calculated and then the time required for the real-time temperature to reach the critical value of the temperature allowable fluctuation range is predicted. The time required for the real-time temperature to reach the critical value of the temperature allowable fluctuation range is compared with the preset adjustment reaction time. If the predicted value is greater than the preset value, no regulation is temporarily performed. If the predicted value is less than or equal to the preset value, the machine temperature is immediately regulated. When the real-time pressure is in the pressure allowable fluctuation range, the present invention determines whether the real-time pressure presents a sudden change, a decreasing or an increasing state. If a sudden change is present, the machine pressure is immediately regulated. If a decrease or an increase is present, the pressure decrease coefficient or the pressure increase coefficient is calculated and then the time required for the real-time pressure to reach the critical value of the pressure allowable fluctuation range is predicted. The time required for the real-time pressure to reach the critical value of the pressure allowable fluctuation range is compared with the preset adjustment reaction time. If the predicted value is greater than the preset value, no regulation is temporarily performed. If the predicted value is less than or equal to the preset value, the machine pressure is immediately regulated. This is beneficial to improving the accuracy of abnormal identification, realizing timely regulation of machine parameters, and reducing abnormal operation time of the machine. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a diagram of the method steps of the present invention.

[0045] Figure 2 It is a system structure block diagram of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] like Figure 1 The present embodiment provides a multi-type status monitoring and early warning method for machine operation monitoring, comprising the following steps:

[0048] S1: Set the temperature fluctuation range or pressure fluctuation range for the machine that needs to control the temperature or pressure during the FPC board processing, set the conveyor speed and cutting speed, and set the motion trajectory, operation point position and operation point position allowable error for the machine that needs to control the positioning accuracy;

[0049] S2: For machines that need to control temperature or pressure during the FPC board processing process, use temperature sensors or pressure sensors to monitor the real-time temperature or real-time pressure of the machine, use speed sensors to monitor the conveyor belt speed and cutting speed, and use cameras to record the movement trajectory and operation point position of the machine that needs to control positioning accuracy;

[0050] S3: Analyze the monitored data to determine whether the machine parameters need to be adjusted or whether there is any abnormality in the machine;

[0051] Further, the specific steps of data analysis in step S3 are as follows:

[0052] S31. When the real-time temperature is in the temperature fluctuation range allowed, determine whether the real-time temperature shows a sudden change, a decreasing or an increasing state. If it shows a sudden change, immediately adjust the machine temperature. If it shows a decreasing or increasing state, calculate the temperature decreasing coefficient or the temperature increasing coefficient and then predict the time required for the real-time temperature to reach the critical value of the temperature fluctuation range allowed. Compare it with the preset adjustment reaction time. If the predicted value is greater than the preset value, do not adjust it temporarily. If the predicted value is less than or equal to the preset value, immediately adjust the machine temperature.

[0053] Furthermore, the specific state judgment process for the real-time temperature within the allowable temperature fluctuation range in step S31 is as follows:

[0054] S311, use T aij It represents the temperature of the i-th machine that needs to control the temperature at the j-th moment. When the temperature of the i-th machine that needs to control the temperature suddenly changes at the j+1th moment, the temperature of the machine needs to be adjusted immediately, σ Ti is the preset temperature mutation coefficient of the i-th machine that needs to control the temperature. It is determined that the temperature of the i-th machine that needs to control the temperature has not changed suddenly at the j+1th moment;

[0055] S312: If the real-time temperature does not suddenly change, determine whether the real-time temperature is decreasing or increasing. or When the real-time temperature decreases or increases, T aij+2 For the temperature of the i-th machine that needs to control the temperature at the j+2th moment, calculate the temperature reduction coefficient δ Ta Or the temperature increase coefficient δ Tb , the specific formula is as follows: , ;

[0056] S313: the predicted time t of the real-time temperature of the i-th machine that needs to control the temperature to reach the critical value of the temperature fluctuation range when the real-time temperature decreases ai The calculation formula is: , T bimin is the minimum allowable temperature of the i-th machine that needs to control the temperature, and the predicted time t for the real-time temperature of the i-th machine that needs to control the temperature to reach the critical value of the temperature fluctuation range when the real-time temperature increases. bi The calculation formula is: , T bimax is the maximum allowable temperature of the i-th machine that needs to control temperature;

[0057] S314, will t ai or bi Adjust the reaction time t yTi For comparison, or The machine temperature is temporarily not regulated, otherwise the machine temperature is immediately regulated. If the subsequent real-time temperature still decreases or increases when the machine temperature is not regulated, the temperature decrease coefficient or temperature increase coefficient and subsequent prediction and judgment results are updated in real time.

[0058] S32. When the real-time pressure is in the pressure fluctuation range, determine whether the real-time pressure shows a sudden change, a decreasing or an increasing state. If it shows a sudden change, the machine pressure is immediately regulated. If it shows a decreasing or increasing state, the pressure decreasing coefficient or the pressure increasing coefficient is calculated to predict the time required for the real-time pressure to reach the critical value of the pressure fluctuation range, and the time is compared with the preset adjustment reaction time. If the predicted value is greater than the preset value, no regulation is performed temporarily. If the predicted value is less than or equal to the preset value, the machine pressure is immediately regulated.

[0059] Furthermore, the specific state judgment process for the real-time pressure within the pressure allowable fluctuation range in step S32 is as follows:

[0060] S321, use P aij It represents the pressure of the i-th machine that needs to control the pressure at the j-th moment. When the pressure of the i-th machine that needs to control the pressure suddenly changes at the j+1th moment, the pressure of the machine needs to be adjusted immediately, σ Pi is the preset pressure mutation coefficient of the i-th machine that needs to control the pressure. It is determined that the pressure of the i-th machine that needs to control the pressure has not changed suddenly at the j+1th moment;

[0061] S322: If the real-time pressure does not suddenly change, determine whether the real-time pressure is decreasing or increasing. or When judging whether the real-time pressure decreases or increases, P aij+2 For the pressure of the i-th machine that needs to control the temperature at the j+2th moment, calculate the pressure reduction coefficient δ Pa Or pressure increase coefficient δ Pb , the specific formula is as follows: , ;

[0062] S323, the predicted time t of the real-time pressure of the i-th machine that needs to control the temperature to reach the critical value of the pressure fluctuation range when the real-time pressure decreases ci The calculation formula is: , P bimin is the minimum allowable pressure of the i-th machine that needs to control pressure, and the predicted time t for the real-time pressure of the i-th machine that needs to control temperature to reach the critical value of the pressure allowable fluctuation range when the real-time pressure increases di The calculation formula is: , P bimax is the maximum allowable pressure of the i-th machine that needs to control pressure;

[0063] S324, will t ci or di Adjust the reaction time t yPi For comparison, or The machine pressure will not be regulated temporarily, otherwise it will be regulated immediately. If the subsequent real-time pressure still decreases or increases when the machine pressure is not regulated, the pressure decrease coefficient or pressure increase coefficient and subsequent prediction and judgment results will be updated in real time.

[0064] S33, when the real-time temperature or real-time pressure exceeds the set temperature allowable fluctuation range or pressure allowable fluctuation range, it is determined that the machine is abnormal;

[0065] S34. Compare the real-time conveyor belt speed, cutting speed, machine motion trajectory, and operation point position with the set parameters. If the real-time conveyor belt speed, cutting speed, and machine motion trajectory are inconsistent with the set values, it is determined that they need to be adjusted. If the error between the real-time operation point position and the set operation point position is less than or equal to the set operation point position allowable error, there is no need to adjust the operation point. If the error between the real-time operation point position and the set operation point position is greater than the set operation point position allowable error, the operation point needs to be adjusted.

[0066] S4: When it is determined that the machine parameters need to be adjusted, the machine enters the automatic adjustment mode, sends the adjustment object, time, and data before and after the adjustment to the management center, and if the automatic adjustment fails, the machine enters the manual adjustment mode, and the management center performs manual adjustment. If the manual adjustment fails, the machine parameter adjustment is determined to be abnormal;

[0067] S5: When it is determined that there is an abnormality in the machine or the machine adjustment is abnormal, the process flow corresponding to the machine will be suspended, and the machine abnormality or the machine parameter adjustment process abnormality will be identified through the trained abnormality recognition model, and the abnormality recognition result will be sent to the management center;

[0068] What needs to be specifically explained in this embodiment is that the training set and test set used when training the abnormality recognition model are the known abnormal manifestations and abnormal causes of the machine operation and machine parameter adjustment process used in the existing FPC board processing process. The working principle of the abnormality recognition model is as follows: first select any mode of machine operation abnormality recognition or machine adjustment process abnormality recognition, and after determining the recognition mode, automatically collect the working parameters in the machine operation process or the various data in the machine parameter adjustment process, and then perform abnormality recognition, and then output the recognition result.

[0069] S6: Arrange maintenance personnel to perform abnormal maintenance based on the abnormal identification results, record the number of machines undergoing abnormal maintenance, the number of machines undergoing normal operation, and the number of abnormal maintenance during each FPC board processing task cycle, and calculate the machine availability coefficient and abnormal occurrence rate during each abnormal maintenance;

[0070] Further, the specific data processing process in step S6 is as follows:

[0071] S61, record the FPC board processing task cycle t z The number of machines undergoing abnormal maintenance within the i-th period m ywi 、Number of machines in normal operation m zyi And the number of abnormal maintenance n yw ;

[0072] S62, the number of machines m that underwent abnormal maintenance for the i-th time during the FPC board processing task cycle ywi and the number of machines in normal operation m zyiCalculate the machine availability rate X during the i-th abnormal maintenance ki , the specific formula is: ;

[0073] S63, based on the FPC board processing task cycle t z And the number of abnormal maintenance times n during the FPC board processing task cycle yw Calculate the abnormality rate X f , the specific formula is: .

[0074] S7: Record the number of machine operation point position errors, the number of machine operation point correct positions, and the operation point position error each time the machine operation point position is wrong within the FPC board processing task cycle to calculate the positioning accuracy control capability coefficient, calculate the theoretical maximum number of FPC board finished products based on the materials used for FPC board processing, and calculate the yield rate based on the actual number of FPC board finished products and the theoretical maximum number of FPC board finished products;

[0075] Further, the specific data processing process in step S7 is as follows:

[0076] S71, record the number of machine operation point position errors m during the FPC board processing task cycle wc 、Number of correct machine operation point positions m wz , the operating point position error ε when the i-th machine operating point has the j-th position error aij , the position allowable error of the i-th machine operating point ε bi 、The theoretical maximum number of finished FPC boards m calculated based on the materials used in FPC board processing cl , and the actual number of finished FPC boards m cs ;

[0077] S72, based on the number of position errors n of the i-th machine operation point in the FPC board processing task cycle wci , the number of correct positions of the i-th machine operating point n wzi , the operating point position error ε when the i-th machine operating point has the j-th position error aij 、The position allowable error εbi of the i-th machine operation point is used to calculate the positioning accuracy control capability coefficient X dc , the specific formula is: , nc is the number of machine operation points;

[0078] S73, based on the theoretical maximum number of finished FPC boards mcl and the actual number of finished FPC boards m cs Calculate the yield rate X cp , the specific formula is: .

[0079] S8: Calculate the average machine availability coefficient based on the average machine availability coefficient, abnormality occurrence rate, positioning accuracy control capability coefficient, and yield rate computer operation quality comprehensive evaluation index within the FPC board processing task cycle.

[0080] Further, the specific data processing process in step S8 is as follows:

[0081] S81, the calculated machine availability rate X during the i-th abnormal maintenance ki After integration, calculate the average machine availability factor X ke , the specific formula is: ;

[0082] S82, based on the average machine availability coefficient, abnormality occurrence rate, positioning accuracy control capability coefficient, and yield rate within the FPC board processing task cycle, the comprehensive evaluation index F of the machine operation quality YZ , the specific calculation formula is: .

[0083] It should be specifically noted in this embodiment that the preset values ​​and coefficients used are selected based on actual needs and are not limited to specific values ​​here.

[0084] like Figure 2 The present embodiment provides a multi-type status monitoring and early warning system for machine operation monitoring, including a basic setting module, an online monitoring module, a monitoring data analysis module, an automatic adjustment module, an abnormality identification module, an abnormality maintenance module, a production and processing data integration module, a machine operation quality comprehensive evaluation module, and a database.

[0085] Basic setting module: Set the temperature fluctuation range or pressure fluctuation range for the machine that needs to control the temperature or pressure during the FPC board processing, set the conveyor speed and cutting speed, and set the motion trajectory, operation point position and operation point position allowable error for the machine that needs to control the positioning accuracy;

[0086] Online monitoring module: For machines that need to control temperature or pressure during the FPC board processing process, use temperature sensors or pressure sensors to monitor the real-time temperature or real-time pressure of the machine, use speed sensors to monitor the conveyor belt speed and cutting speed, and use cameras to record the movement trajectory and operation point position of the machine that needs to control positioning accuracy;

[0087] Monitoring data analysis module: Analyze the monitored data to determine whether the machine parameters need to be adjusted or whether there are any abnormalities in the machine;

[0088] Automatic adjustment module: When it is determined that the machine parameters need to be adjusted, it enters the automatic adjustment mode and sends the adjustment object, time, and data before and after the adjustment to the management center. If the automatic adjustment fails, it switches to the manual adjustment mode and the management center performs manual adjustment. If the manual adjustment fails, it is determined that the machine parameter adjustment is abnormal.

[0089] Abnormal identification module: When it is determined that there is an abnormality in the machine or the machine adjustment is abnormal, the corresponding process flow of the machine will be suspended. The machine abnormality or machine parameter adjustment process abnormality will be identified through the trained abnormality identification model, and the abnormality identification result will be sent to the management center;

[0090] Abnormal maintenance module: Arrange maintenance personnel to perform abnormal maintenance based on abnormal identification results, record the number of abnormal maintenance machines, the number of normal operating machines and the number of abnormal maintenance times during each FPC board processing task cycle, and calculate the machine availability coefficient and abnormal occurrence rate during each abnormal maintenance;

[0091] Production and processing data integration module: records the number of machine operation point position errors, the number of correct machine operation point positions, and the operation point position error calculation positioning accuracy control capability coefficient each time the machine operation point position is wrong within the FPC board processing task cycle, calculates the theoretical maximum number of FPC board finished products based on the materials used for FPC board processing, and calculates the yield rate based on the actual number of FPC board finished products and the theoretical maximum number of FPC board finished products;

[0092] Machine operation quality comprehensive evaluation module: calculate the average machine availability coefficient, based on the average machine availability coefficient, abnormality occurrence rate, positioning accuracy control capability coefficient, and yield rate within the FPC board processing task cycle to calculate the machine operation quality comprehensive evaluation index;

[0093] Database: used to store data information of all modules in the system.

[0094] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-type status monitoring and early warning method for machine operation monitoring, characterized in that: The following steps are involved: S1: Set the temperature fluctuation range or pressure fluctuation range for the machine that needs to control the temperature or pressure during the FPC board processing, set the conveyor speed and cutting speed, and set the motion trajectory, operation point position and operation point position allowable error for the machine that needs to control the positioning accuracy; S2: For machines that need to control temperature or pressure during the FPC board processing process, use temperature sensors or pressure sensors to monitor the real-time temperature or real-time pressure of the machine, use speed sensors to monitor the conveyor belt speed and cutting speed, and use cameras to record the movement trajectory and operation point position of the machine that needs to control positioning accuracy; S3: Analyze the monitored data to determine whether the machine parameters need to be adjusted or whether there is any abnormality in the machine; S4: When it is determined that the machine parameters need to be adjusted, the machine enters the automatic adjustment mode, sends the adjustment object, time, and data before and after the adjustment to the management center, and if the automatic adjustment fails, the machine enters the manual adjustment mode, and the management center performs manual adjustment. If the manual adjustment fails, the machine parameter adjustment is determined to be abnormal; S5: When it is determined that there is an abnormality in the machine or the machine adjustment is abnormal, the process flow corresponding to the machine will be suspended, and the machine abnormality or the machine parameter adjustment process abnormality will be identified through the trained abnormality recognition model, and the abnormality recognition result will be sent to the management center; S6: Arrange maintenance personnel to perform abnormal maintenance based on the abnormal identification results, record the number of machines undergoing abnormal maintenance, the number of machines undergoing normal operation, and the number of abnormal maintenance during each FPC board processing task cycle, and calculate the machine availability coefficient and abnormal occurrence rate during each abnormal maintenance; S7: Record the number of machine operation point position errors, the number of machine operation point correct positions, and the operation point position error each time the machine operation point position is wrong within the FPC board processing task cycle to calculate the positioning accuracy control capability coefficient, calculate the theoretical maximum number of FPC board finished products based on the materials used for FPC board processing, and calculate the yield rate based on the actual number of FPC board finished products and the theoretical maximum number of FPC board finished products; S8: Calculate the average machine availability coefficient based on the average machine availability coefficient, abnormality occurrence rate, positioning accuracy control capability coefficient, and yield rate computer operation quality comprehensive evaluation index within the FPC board processing task cycle.

2. A multi-type status monitoring and early warning method for machine operation monitoring according to claim 1, characterized in that: The specific steps of data analysis in step S3 are as follows: S31. When the real-time temperature is in the temperature fluctuation range allowed, determine whether the real-time temperature shows a sudden change, a decreasing or an increasing state. If it shows a sudden change, immediately adjust the machine temperature. If it shows a decreasing or increasing state, calculate the temperature decreasing coefficient or the temperature increasing coefficient and then predict the time required for the real-time temperature to reach the critical value of the temperature fluctuation range allowed. Compare it with the preset adjustment reaction time. If the predicted value is greater than the preset value, do not adjust it temporarily. If the predicted value is less than or equal to the preset value, immediately adjust the machine temperature. S32. When the real-time pressure is in the pressure fluctuation range, determine whether the real-time pressure shows a sudden change, a decreasing or an increasing state. If it shows a sudden change, the machine pressure is immediately regulated. If it shows a decreasing or increasing state, the pressure decreasing coefficient or the pressure increasing coefficient is calculated to predict the time required for the real-time pressure to reach the critical value of the pressure fluctuation range, and the time is compared with the preset adjustment reaction time. If the predicted value is greater than the preset value, no regulation is performed temporarily. If the predicted value is less than or equal to the preset value, the machine pressure is immediately regulated. S33, when the real-time temperature or real-time pressure exceeds the set temperature allowable fluctuation range or pressure allowable fluctuation range, it is determined that the machine is abnormal; S34. Compare the real-time conveyor belt speed, cutting speed, machine motion trajectory, and operation point position with the set parameters. If the real-time conveyor belt speed, cutting speed, and machine motion trajectory are inconsistent with the set values, it is determined that they need to be adjusted. If the error between the real-time operation point position and the set operation point position is less than or equal to the set operation point position allowable error, there is no need to adjust the operation point. If the error between the real-time operation point position and the set operation point position is greater than the set operation point position allowable error, the operation point needs to be adjusted.

3. A multi-type status monitoring and early warning method for machine operation monitoring according to claim 2, characterized in that: The specific state judgment process for the real-time temperature within the allowable temperature fluctuation range in step S31 is as follows: S311, use T aij It represents the temperature of the i-th machine that needs to control the temperature at the j-th moment. When the temperature of the i-th machine that needs to control the temperature suddenly changes at the j+1th moment, the temperature of the machine needs to be adjusted immediately, σ Ti is the preset temperature mutation coefficient of the i-th machine that needs to control the temperature. It is determined that the temperature of the i-th machine that needs to control the temperature has not changed suddenly at the j+1th moment; S312: If the real-time temperature does not suddenly change, determine whether the real-time temperature is decreasing or increasing. or When the real-time temperature decreases or increases, T aij+2 For the temperature of the i-th machine that needs to control the temperature at the j+2th moment, calculate the temperature reduction coefficient δ Ta Or the temperature increase coefficient δ Tb , the specific formula is as follows: , ; S313: the predicted time t of the real-time temperature of the i-th machine that needs to control the temperature to reach the critical value of the temperature fluctuation range when the real-time temperature decreases ai The calculation formula is: , T bimin is the minimum allowable temperature of the i-th machine that needs to control the temperature, and the predicted time t for the real-time temperature of the i-th machine that needs to control the temperature to reach the critical value of the temperature fluctuation range when the real-time temperature increases. bi The calculation formula is: , T bimax is the maximum allowable temperature of the i-th machine that needs to control temperature; S314, will t ai or bi The preset adjustment reaction time t yTi For comparison, or The machine temperature is temporarily not regulated, otherwise the machine temperature is immediately regulated. If the subsequent real-time temperature still decreases or increases when the machine temperature is not regulated, the temperature decrease coefficient or temperature increase coefficient and subsequent prediction and judgment results are updated in real time.

4. The multi-type status monitoring and early warning method for machine operation monitoring according to claim 2 is characterized in that: The specific state judgment process for the real-time pressure within the pressure fluctuation range in step S32 is as follows: S321, use P aij It represents the pressure of the i-th machine that needs to control the pressure at the j-th moment. When the pressure of the i-th machine that needs to control the pressure suddenly changes at the j+1th moment, the pressure of the machine needs to be adjusted immediately, σ Pi is the preset pressure mutation coefficient of the i-th machine that needs to control the pressure. It is determined that the pressure of the i-th machine that needs to control the pressure has not changed suddenly at the j+1th moment; S322: If the real-time pressure does not suddenly change, determine whether the real-time pressure is decreasing or increasing. or When judging whether the real-time pressure decreases or increases, P aij+2 For the pressure of the i-th machine that needs to control the temperature at the j+2th moment, calculate the pressure reduction coefficient δ Pa Or pressure increase coefficient δ Pb , the specific formula is as follows: , ; S323, the predicted time t of the real-time pressure of the i-th machine that needs to control the temperature to reach the critical value of the pressure fluctuation range when the real-time pressure decreases ci The calculation formula is: , P bimin is the minimum allowable pressure of the i-th machine that needs to control pressure, and the predicted time t for the real-time pressure of the i-th machine that needs to control temperature to reach the critical value of the pressure allowable fluctuation range when the real-time pressure increases di The calculation formula is: , P bimax is the maximum allowable pressure of the i-th machine that needs to control pressure; S324, will t ci or di The preset adjustment reaction time t yPi For comparison, or The machine pressure will not be regulated temporarily, otherwise it will be regulated immediately. If the subsequent real-time pressure still decreases or increases when the machine pressure is not regulated, the pressure decrease coefficient or pressure increase coefficient and subsequent prediction and judgment results will be updated in real time.

5. The multi-type status monitoring and early warning method for machine operation monitoring according to claim 1 is characterized in that: The specific data processing process in step S6 is as follows: S61, record the FPC board processing task cycle t z The number of machines undergoing abnormal maintenance within the i-th period m ywi 、Number of machines in normal operation m zyi And the number of abnormal maintenance n yw ; S62, the number of machines m that underwent abnormal maintenance for the i-th time during the FPC board processing task cycle ywi and the number of machines in normal operation m zyi Calculate the machine availability rate X during the i-th abnormal maintenance ki , the specific formula is: ; S63, based on the FPC board processing task cycle t z And the number of abnormal maintenance times n during the FPC board processing task cycle yw Calculate the abnormality rate X f , the specific formula is: .

6. A multi-type status monitoring and early warning method for machine operation monitoring according to claim 5, characterized in that: The specific data processing process in step S7 is as follows: S71, record the number of machine operation point position errors m during the FPC board processing task cycle wc 、Number of correct machine operation point positions m wz , the operating point position error ε when the i-th machine operating point has the j-th position error aij , the position allowable error of the i-th machine operating point ε bi 、The theoretical maximum number of finished FPC boards m calculated based on the materials used in FPC board processing cl , and the actual number of finished FPC boards m cs ; S72, based on the number of position errors n of the i-th machine operation point in the FPC board processing task cycle wci , the number of correct positions of the i-th machine operating point n wzi , the operating point position error ε when the i-th machine operating point has the j-th position error aij 、The position allowable error εbi of the i-th machine operation point is used to calculate the positioning accuracy control capability coefficient X dc , the specific formula is: , nc is the number of machine operation points; S73, based on the theoretical maximum number of finished FPC boards mcl and the actual number of finished FPC boards m cs Calculate the yield rate X cp , the specific formula is: .

7. A multi-type status monitoring and early warning method for machine operation monitoring according to claim 6, characterized in that: The specific data processing process in step S8 is as follows: S81, the calculated machine availability rate X during the i-th abnormal maintenance ki After integration, calculate the average machine availability factor X ke , the specific formula is: ; S82, based on the average machine availability coefficient, abnormality occurrence rate, positioning accuracy control capability coefficient, and yield rate within the FPC board processing task cycle, the comprehensive evaluation index F of the machine operation quality YZ , the specific calculation formula is: .

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