CN114943524A - Equipment production state monitoring system and working method thereof - Google Patents
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Abstract
The invention discloses an equipment production state monitoring system, which comprises an equipment production state monitor, a factory local area network, a client computer and a server computer, wherein the equipment production state monitor is communicated with the client computer and the server computer through the factory local area network; the equipment production state monitor comprises a current sensing module, an A/D conversion module, a data storage module, a start-up code scanning module, a main control monitoring module, a touch display screen, an abnormity alarm module and a communication transmission module. The invention judges the real-time data by utilizing the characteristic parameters, realizes the real-time monitoring of the production process of the parts, can quickly and effectively calibrate the process characteristics, can automatically calculate the parameter values of each characteristic only by collecting a certain amount of data, and finally generates the process characteristic file by manual determination.
Description
Technical Field
The invention relates to the technical field of manufacturing production informatization, in particular to a system for monitoring the production state of equipment and a working method thereof.
Background
With the continuous deepening of the intelligent manufacturing concept, the manufacturing enterprises are accelerated to change and upgrade towards manufacturing digitization, informatization and intelligence. The manufacturing workshop site has a large amount of data information, equipment state information such as processing, standby, halt and the like can be generated after the equipment is put into use, information such as rotating speed, feeding speed, abrasion degree, service life and the like can be generated when a cutter is used for cutting, detailed production state information such as size, quantity, quality, processing period and start operation can be generated when a single part is processed, and information such as batch and process can be generated when a container is circulated. Most of the extraction and processing of the production data information are in manual collection and manual entry stages, the authenticity of the information cannot be verified, and the utilization rate is low. Although the uploading, the storage, the inquiry and the utilization of the data are greatly improved by using the manufacturing execution system, and part of production data recorded among different processes can be mutually checked, the timeliness is poor, abnormal processing information cannot be fed back in time, the labor is excessively relied on, the accuracy of the data cannot be guaranteed, and the waste of additional resources such as data error correction is caused.
The application of the equipment production state detection system can effectively relieve the pressure of data acquisition and uploading of staff in a processing field, prompt abnormal state information of equipment in time and quickly maintain the equipment to recover production. Through uploading production report data to the server, managers can supervise workshop production more efficiently, and management efficiency is improved.
Disclosure of Invention
Aiming at the defects that the accuracy of a monitoring result is reduced due to insufficient offline analysis and inaccurate calibration links, the invention designs a set of equipment production state monitor, calibrates process characteristics and repeatedly adjusts parameters through offline analysis of current data until characteristic parameters can accurately identify equipment production information corresponding to the offline data. And then, the process characteristics are used for equipment state recognition, the operation information of the equipment is obtained, the operation information comprises the starting time and the completion time of the part, the current state of the equipment, an equipment abnormal mark and the like, and the information of the part processing quality, the equipment energy consumption, the cutter abrasion degree, the fault and the like is further obtained. The method provides a basis for the maintenance of equipment, the formulation of production plans, the management of energy consumption, the replacement of cutters and the efficient inspection of the quality of parts.
In order to achieve the purpose, the invention adopts the following technical scheme:
an equipment production state monitoring system comprises an equipment production state monitor, a factory local area network, a client computer and a server computer, wherein the equipment production state monitor is communicated with the client computer and the server computer through the factory local area network; the equipment production state monitor comprises a current sensing module, an A/D conversion module, a data storage module, a start-up code scanning module, a main control monitoring module, a touch display screen, an abnormity alarm module and a communication transmission module;
the current sensing module is connected with the main control monitoring module through the A/D conversion module, converts the acquired current analog signal into a digital signal and transmits the digital signal to the main control monitoring module;
the data storage module is connected with the main control monitoring module through an ST interface and is responsible for storing current digital signals obtained after A/D conversion;
the start-up code scanning module is connected with the master control monitoring module through a USB interface and is responsible for scanning the process characteristic two-dimensional code and the production plan two-dimensional code to obtain process characteristic information and production plan information and transmitting the obtained information to the master control monitoring module;
the touch display screen is connected with the main control monitoring module through an HDMI (high-definition multimedia interface), and is responsible for displaying real-time monitoring result information of the main control monitoring module and receiving a working instruction given by a user in a touch control mode;
the abnormity alarm module is connected with the main control monitoring module through an RS485 interface, and when the abnormal production information is monitored, a buzzer is used for carrying out sound alarm;
the main control monitoring module uploads the monitored equipment production information to a server computer connected with a factory local area network through a communication transmission module.
Furthermore, a database system is operated in the server computer and comprises a process characteristic database, a production plan database and a production record database.
Further, each record of the process characteristic database comprises 1 process characteristic code and 6 characteristic parameters: threshold value V for start-up strt Completion threshold V end Standby threshold T wit Machining period T work Standard current and V totl Standard mean current V vg Wherein the process feature code is a unique primary key field; each record of the production plan database comprises 3 fields of a production plan order number, a process name and a plan number, wherein the production order number is a unique main key field; the production record database records the production record information of each part obtained by the equipment production state monitor 1 through real-time monitoring, and the production record information comprises 4 fields of a production plan order number, a machining starting time, a machining ending time and a production abnormity mark.
Further, the management software (2.2) running on the client computer comprises the following functions: downloading a current data file from the equipment production state monitor 1, inquiring the state information of the equipment monitored by the equipment production state monitor, calibrating the process characteristics according to a production process characteristic calibration algorithm, storing the calibrated process characteristics into a process characteristic database, inquiring information in the database system, printing a process characteristic two-dimensional code and printing a production plan two-dimensional code.
A working method of a monitoring system for the production state of equipment,
step 1, electrifying and starting up the equipment production state monitor, and selecting working modes through a touch display screen, wherein the selection comprises two working modes: calibrating a data acquisition mode and a real-time production monitoring mode;
step 2, carrying out a calibration data acquisition working mode, which comprises two working instructions: the method comprises the steps that a calibration data acquisition instruction is started, the calibration data acquisition instruction is finished, after the calibration data acquisition instruction is started to be issued, a main control monitoring module stores a current digital signal transmitted by an A/D conversion module to a data storage module, during storage, the time of starting acquisition is used as the file name of a current data file, and after the calibration data acquisition instruction is finished to be issued, acquisition and data storage are stopped;
step 3, a user downloads an obtained current data file through a communication transmission module of the equipment production state monitor by using management software running on a client computer, a management system performs characteristic calibration of the production process by using a production process characteristic calibration algorithm according to the current data file, the management system stores a calibration result of the process characteristics into a process characteristic database, each production process characteristic record is identified by using a unique process characteristic code, and an increasing sequence digital code is used as a process characteristic code;
step 4, carrying out a real-time production monitoring working mode of the equipment production state monitor, wherein the working mode comprises two stages of code scanning start and real-time monitoring, in the code scanning start, a start code scanning module scans a production plan two-dimensional code and a process characteristic two-dimensional code in sequence, transmits the process characteristic information and the production plan information obtained by code scanning to a main control monitoring module, and then enters the real-time monitoring stage;
step 5, in the real-time monitoring stage of the equipment production state monitor in a real-time production monitoring working mode, the main control monitoring module stores the current digital signals transmitted by the A/D conversion module into the data storage module, and performs real-time analysis by using the process characteristic information and the production plan information and adopting an equipment real-time monitoring algorithm to obtain equipment operation information, wherein the equipment operation information comprises equipment state information and equipment production information, and the equipment state information comprises a state type and a state occurrence moment; the equipment production information comprises a production plan order code, machining starting time, machining ending time and a production abnormal mark of the part; the device state types include three states: stopping, standby and processing; production anomaly markers include four markers: too long processing time, too short processing time, too large processing current and too small processing current.
Step 6, in the real-time monitoring stage of the equipment production state monitor in the real-time production monitoring working mode, the main control monitoring module performs production reporting and abnormal alarming according to the equipment operation information obtained through real-time analysis, wherein the production reporting refers to a process that the main control monitoring module uploads the monitored equipment production information to a production record database on a server computer through a communication transmission module in the real-time production monitoring working mode; the abnormal alarm is as follows: judging whether the monitored equipment production information has production abnormity, if the production abnormity exists, performing sound alarm through an abnormity alarm module to prompt a user that the equipment production is abnormal;
and 7, in the real-time monitoring stage of the equipment production state monitor in the real-time production monitoring working mode, the master control monitoring module displays equipment state information on a touch display screen according to the equipment running information obtained by real-time analysis, wherein the equipment state information comprises three equipment state types including shutdown, standby and processing and state query time.
Further, the production process characteristic calibration algorithm in the step 3 comprises the following steps:
step 3.1, aiming at the standby state and the processing state of the equipment, calculating a start threshold V of the processing state of the equipment by adopting a threshold algorithm for distinguishing the processing state and the standby state of the equipment strt Completion threshold V end ；
Step 3.2, aiming at the two states of feeding and discharging and tool changing in the standby state of the equipment, a standby threshold value algorithm for distinguishing the tool changing state and the feeding and discharging state is adopted to calculate a standby threshold value T wit After the feeding and discharging are finished, the staff take out the processed parts in the equipment and place new parts; changing the cutter, namely changing the cutter in the machining process;
step 3.3, according to the start-up threshold V strt Completion threshold V end Standby threshold T wit Segmenting the acquired current data, wherein each segment of data represents a complete part machining process, calculating the machining time of each part according to the machining starting point and the machining ending point of the part obtained after segmentation, removing abnormal values, and taking a median value as the machining period T of the part work (ii) a Accumulating and summing currents in the processing time of each part to obtain a current sum, dividing the current sum by the number of sampling points to obtain an average current, removing abnormal values, and taking a median to obtain a standard current sum V totl And standard mean current V vg ；
Step 3.4, 6 production characteristic parameters V obtained by calculation from the step 3.1 to the step 3.3 strt 、V end 、T wit 、T work 、V totl 、V vg And uploading the data to a process characteristic database in a server computer.
Further, the real-time monitoring algorithm of the equipment in the step 5 comprises the following steps:
step 5.1, according to the production process characteristic parameter V obtained by the start-up code scanning module strt 、V end 、T wit 、T work 、V totl 、V vg Setting a standard average current V vg Tolerance V, standard current and V totl Coefficient of value f1, f2, f3, striker threshold V H ；
Step 5.2, the main control monitoring module additionally stores the real-time acquired equipment current value into an array list, and the step 5.3 is carried out;
step 5.3, if the current value is larger than the cutter collision threshold value V H The method comprises the following steps of (1) indicating that a tool bumping accident occurs in the production process of equipment, giving a production abnormal identifier of large machining current by an algorithm, and indicating the shutdown state of the equipment if the current value is equal to 0; if the current value is increased rapidly from 0 and tends to be stable, the equipment is started, and the starting time is recorded by an algorithm; if the current is changed from the large value to 0, the equipment is shut down, the algorithm records the shutdown time, and the step 5.4 is carried out;
step 5.4, if the number of the collected data points is less than T work +T wit Go to step 5.2; if the number of the collected data points is more than or equal to T work +T wit If so, then pair with the last previous T work Analyzing the data, and judging the start-up threshold V strt Whether the size is between the first element list [0 ] of the segment of data]With the adjacent element list [1 ]]If yes, it indicates the start of work, and list [1 ]]Taking the corresponding moment as the start time and recording, turning to step 5.5, otherwise, deleting the first element list [ 2 ]0]Go to step 5.2;
step 5.5, judging T before list work Whether the average value of the individual data is in the interval [ V ] vg -v，V vg +v]If yes, the processing is normal, and the step 5.6 is carried out; otherwise, indicating that the processing is abnormal, and turning to the step 5.7;
step 5.6, if list [ T ] work ]Less than V end From list [ T work ]To list [ T ] work -1]Direction and completion threshold V end Comparing one by one until the current value is larger than V end Indicating completion and recording completion time; if list [ T ] work ]Greater than V end From list [ T ] work ]To list [ T ] work +1]Direction and completion threshold V end Comparing one by one until the current value is less than V end Completing the record; if list [ T ] work ]To list [ T ] work +T wit ]All values in are greater than V end With T work +T wit Deleting all data in the list by taking the corresponding moment as the completion time, and turning to the step 7.8;
step 5.7, judging the total current of the section of data, if the total current is larger than f 1V totl From list [ T work ]To list [ T ] work +1]Direction and completion threshold V end Comparing one by one, and synchronizing the steps 5.6; if less than f 2V totl And is greater than f 3V totl From list [ T work ]To list [ T ] work -1]Direction and completion threshold V end Comparing one by one, and if the threshold interval is not met, indicating abnormal fluctuation in the standby state, going to the step 5.2;
and 5.8, recording the start time, the completion time and the production abnormal mark of the part, and ending the algorithm.
Further, the threshold algorithm for distinguishing the device processing state from the standby state in step 3.1 is specifically as follows:
taking a section { p '} and { p' } in the current data sequence { p } which at least comprises 5 complete part processing processes, finding the positions of the maximum value and the minimum value, searching the minimum value x1 of the left neighborhood of the maximum value and the minimum value x2 of the right neighborhood, making x ═ mx { x1 and x2}, and similarly, searching the maximum value z1 in the minimum value field,z2, where z is min { z1, z2}, and x and z respectively represent fluctuation of the machining-state current around x and fluctuation of the standby-state current around z, and the values of x and z are adjusted to obtain the start-up threshold V strt Completion threshold V end 。
Further, the algorithm of the standby threshold for distinguishing the tool changing state and the loading and unloading state in step 3.2 is specifically as follows:
according to the start-up threshold V strt And completion threshold V end Calculating all standby Time { Time } in the current data sequence { p' }, including loading and unloading, tool changing and abnormal fluctuation Time, as follows: traversing the data { p' }, and judging whether the current value is less than V or not end If yes, start to count num + + until the current value is larger than V strt The num value is stored in Time'. Removing abnormal values according to actual conditions and calculating standby threshold T in a classified mode wit (ii) a If there is a tool change, then T wit Taking m between the time of feeding and discharging and tool changing, if no tool changing, T wit Taking the value n, wherein n is larger than 0 and smaller than the minimum value of { Time' }.
Further, the data segmentation method in step 3.3 is specifically as follows:
according to the start-up threshold V strt Completion threshold V end Standby threshold T wit Segmenting the collected current data, calculating the following steps, traversing the data { p }, calculating by using the method in the step 6.2 to obtain a standby Time set { Time }, a standby starting point set { pws }, a standby ending point set { pwe }, and deleting less than T in the { Time } wit The corresponding data in the pws and pwe are adjusted to obtain the processing starting point and the processing ending point of all the middle parts, and the segmentation is realized
The invention has the beneficial effects that: the method can segment data according to the characteristic that the current fluctuates sharply around a large value and steadily around a small value when the equipment is used for processing parts and the characteristic that the manual feeding and discharging time is obviously longer than the tool changing time, extract the production state characteristic information of the equipment and form a process characteristic file. And judging the real-time data by using the characteristic parameters to realize the real-time monitoring of the production process of the part. The method can quickly and effectively calibrate the process characteristics, can automatically calculate the parameter values of all the characteristics only by collecting a certain amount of data, and finally generates the process characteristic file through manual determination. The process characteristic file is manually modified through a simulation monitoring test or feedback of field staff, so that the characteristic value is more accurate.
The running information of the equipment can be displayed on the display screen, and comprises start-up time, completion time, equipment state, current time, abnormal information and the like, the total working time, the stop time and the proportion of the equipment, the total processing time, the total standby time and the proportion of the equipment, the current state and the current time of the equipment, the number of processed parts, the processing time and the standby time of each part, the processing efficiency of the parts, the processing qualified rate and the like are further obtained, the running information of the equipment is stored to the local and uploaded to a production record database in the server through a network. Through corresponding instructions, the production records and the current state information of the equipment can be inquired. All levels of departments of the enterprise can optimize production plans according to the equipment operation information and derivative information thereof, strictly calculate production working hours, check daily working loads of the equipment, trace quality problem parts to specific equipment, specific equipment and specific time, realize the visualization of the production state of the equipment and provide data support for management decisions of the enterprise.
Drawings
FIG. 1 is a block diagram showing the overall configuration of a system for monitoring the production state of a plant
FIG. 2 is a block diagram of a plant production status monitor.
Fig. 3 is a flow chart of information transmission of the equipment production state monitoring system.
FIG. 4 is equipment operational information obtained by the equipment production status monitoring system.
FIG. 5 is a flow chart of a process feature calibration algorithm.
Fig. 6 is a flow chart of a real-time monitoring algorithm for the status of the device.
Fig. 7 is a graph of raw current data collected from a process of the part 1.
Fig. 8 is a plot of raw current data collected for a process for part 2.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, an apparatus production state monitoring system includes an apparatus production state monitor 1, a factory lan, a client computer, and a server computer, the apparatus production state monitor 1 communicating with the client computer and the server computer via the factory lan; as shown in fig. 2, the device production state monitor comprises a current sensing module 1.1, an a/D conversion module 1.2, a data storage module 1.3, a start code scanning module 1.4, a master control monitoring module 1.5, a touch display screen 1.6, an abnormality alarm module 1.7 and a communication transmission module 1.8;
the current sensing module 1.1 is connected with the main control monitoring module 1.5 through an A/D conversion module 1.2, converts the acquired current analog signal into a digital signal and transmits the digital signal to the main control monitoring module 1.5;
the data storage module 1.3 is connected with the main control monitoring module 1.5 through an ST interface and is responsible for storing current digital signals obtained after A/D conversion;
the start-up code scanning module 1.4 is connected with the master control monitoring module 1.5 through a USB interface, and is responsible for scanning the process characteristic two-dimensional code and the production plan two-dimensional code, acquiring process characteristic information and production plan information, and transmitting the acquired information to the master control monitoring module 1.5;
the touch display screen 1.6 is connected with the main control monitoring module 1.5 through an HDMI interface, and is responsible for displaying real-time monitoring result information of the main control monitoring module 1.5 and receiving a working instruction given by a user in a touch control mode;
the abnormal alarm module 1.7 is connected with the main control monitoring module 1.5 through an RS485 interface, and when the abnormal production information is monitored, a buzzer is used for carrying out sound alarm;
the main control monitoring module 1.5 uploads the monitored equipment production information to a server computer connected with a factory local area network through a communication transmission module 1.8.
As shown in FIG. 3, database system 2.3 is run in the server computer, and includes process characteristics database 2.3.1, production plan database 2.3.2, and production schedule databaseA production record database 2.3.3, wherein each record of the process characteristic database 2.3.1 comprises 1 process characteristic code and 6 characteristic parameters: threshold value V for start-up strt Completion threshold V end Standby threshold T wit Machining period T work Standard current and V totl Standard mean current V vg Wherein the process feature code is a unique primary key field.
Each record of the production plan database 2.3.2 includes 3 fields for production plan order number, process name, and number of plans, where the production order number is the unique primary key field.
The production record database 2.3.3 records the production record information of each part obtained by the real-time monitoring of the equipment production state monitor 1, and the production record information comprises 4 fields of a production plan order number, a machining starting time, a machining finishing time and a production abnormity mark.
Management software 2.2 running on the client computer, comprising the following functions: downloading a current data file from the equipment production state monitor 1, inquiring the state information of the equipment monitored by the equipment production state monitor 1, calibrating the process characteristics according to a production process characteristic calibration algorithm, storing the calibrated process characteristics into a process characteristic database, inquiring information in a database system 2.3, printing a process characteristic two-dimensional code and printing a production plan two-dimensional code.
A working method of a device production state monitoring system,
step 1, the equipment production state monitor 1 is powered on and started up, and the selection of working modes is carried out through a touch display screen 1.6, and the equipment production state monitor comprises two working modes: calibrating a data acquisition mode and a real-time production monitoring mode;
step 2, carrying out a calibration data acquisition working mode, comprising two working instructions: the method comprises the steps that a calibration data acquisition instruction is started, the calibration data acquisition instruction is finished, after the calibration data acquisition instruction is started to be issued, a main control monitoring module 1.5 stores a current digital signal transmitted by an A/D conversion module 1.2 to a data storage module 1.3, during storage, the time of starting acquisition is used as the file name of a current data file, and after the calibration data acquisition instruction is finished to be issued, acquisition and data storage are stopped;
step 3, a user downloads an obtained current data file by using management software 2.2 running on a client computer through a communication transmission module 1.8 of the equipment production state monitor 1, the management system 2.2 performs characteristic calibration of the production process by using a production process characteristic calibration algorithm according to the current data file, the management system 2.2 stores a calibration result of the process characteristic to a process characteristic database 2.3.1, each production process characteristic record is identified by using a unique process characteristic code, and an increasing sequence digital code is used as a process characteristic code;
step 4, a real-time production monitoring working mode of the equipment production state monitor 1 is carried out, the working mode comprises two stages of code scanning and starting and real-time monitoring, in the code scanning and starting stage, a working code scanning module 1.4 sequentially scans a production plan two-dimensional code and a process characteristic two-dimensional code, the process characteristic information and the production plan information obtained by code scanning are transmitted to a main control monitoring module 1.5, and then the real-time monitoring stage is started;
and 5, in the real-time monitoring stage of the equipment production state monitor 1 in the real-time production monitoring working mode, the main control monitoring module 1.5 stores the current digital signals transmitted by the A/D conversion module 1.2 into the data storage module 1.3, and performs real-time analysis by using the process characteristic information and the production plan information and adopting an equipment real-time monitoring algorithm to obtain equipment operation information. As shown in fig. 4, the device operation information includes device status information and device production information, and the device status information includes a status type and a status occurrence time; the equipment production information comprises a production plan order code, machining starting time, machining ending time and a production abnormal mark of the part; the device state types include three states: stopping, standby and processing; production anomaly markers include four markers: too long processing time, too short processing time, too large processing current and too small processing current.
Step 6, in the real-time monitoring stage of the equipment production state monitor 1 in the real-time production monitoring working mode, the main control monitoring module 1.5 performs production reporting and abnormal alarming according to the equipment operation information obtained by real-time analysis, wherein the production reporting refers to a process that the main control monitoring module 1.5 uploads the monitored equipment production information to the production record database 2.3.3 on the server computer through the communication transmission module 1.8 in the real-time production monitoring working mode; the abnormal alarm is as follows: judging whether the monitored equipment production information has production abnormity, if the production abnormity exists, performing sound alarm through an abnormity alarm module 1.7 to prompt a user that the equipment production is abnormal;
and 7, in the real-time monitoring stage of the equipment production state monitor 1 in the real-time production monitoring working mode, the main control monitoring module 1.5 displays equipment state information on the touch display screen 1.6 according to the equipment running information obtained by real-time analysis, wherein the equipment state information comprises three equipment state types including shutdown, standby and processing and state query time.
As shown in fig. 5, the steps of the calibration algorithm for the characteristics of the production process in step 3 are as follows:
step 3.1, aiming at the standby state and the processing state of the equipment, calculating a start threshold V of the processing state of the equipment by adopting a threshold algorithm for distinguishing the processing state and the standby state of the equipment strt Completion threshold V end ；
Step 3.2, aiming at the two states of feeding and discharging and tool changing in the standby state of the equipment, a standby threshold value algorithm for distinguishing the tool changing state and the feeding and discharging state is adopted to calculate a standby threshold value T wit After the feeding and discharging are finished, the staff take out the processed parts in the equipment and place new parts; changing the cutter, namely changing the cutter in the machining process;
step 3.3, according to the start-up threshold V strt Completion threshold V end Standby threshold T wit Segmenting the collected current data, wherein each segment of data represents a complete part machining process, calculating the machining time of each part according to the machining starting point and the machining ending point of the part obtained after segmentation, removing abnormal values, and taking the median as the machining period T of the part work (ii) a Accumulating and summing currents in the processing time of each part to obtain a current sum, dividing the current sum by the number of sampling points to obtain an average current, removing abnormal values, and taking a median to obtain a standard current sum V totl And standard mean current V vg ；
Step 3.4, 6 production characteristic parameters V obtained by calculation in the step 3.1 to the step 3.3 strt 、V end 、T wit 、T work 、V totl 、V vg And uploading the data to a process characteristic database 2.3.1 in a server computer.
As shown in fig. 6, the steps of the real-time monitoring algorithm for the device in step 5 are as follows:
step 5.1, according to the production process characteristic parameter V obtained by the start-up code scanning module 1.4 strt 、V end 、T wit 、T work 、V totl 、V vg Setting a standard average current V vg Tolerance V, standard current and V of totl Value coefficients f1, f2, f3, striker threshold V H ；
Step 5.2, the main control monitoring module 1.5 additionally stores the real-time acquired equipment current value into an array list, and the step 5.3 is carried out;
step 5.3, if the current value is larger than the cutter collision threshold value V H The method comprises the following steps of (1) indicating that a tool bumping accident occurs in the production process of equipment, giving a production abnormal identifier of large machining current by an algorithm, and indicating the shutdown state of the equipment if the current value is equal to 0; if the current value is increased sharply from 0 and tends to be stable, the equipment is started, and the starting time is recorded by an algorithm; if the current is changed from the large value to 0, the equipment is shut down, the algorithm records the shutdown time, and the step 5.4 is carried out;
step 5.4, if the number of the collected data points is less than T work +T wit Go to step 5.2; if the number of the collected data points is more than or equal to T work +T wit If so, then pair with the last previous T work Analyzing the data, and judging the start-up threshold V strt Whether the size is between the first element list [0 ] of the segment of data]With the adjacent element list [1 ]]If yes, it indicates the start of work, and list [1 ]]Taking the corresponding moment as the start time and recording, turning to the step 5.5, otherwise, deleting the first element list [0 ]]Go to step 5.2;
step 5.5, judging T before list work Whether the average value of the individual data is in the interval [ V ] vg -v，V vg +v]If yes, the processing is normal, and the step 5.6 is carried out;otherwise, indicating that the processing is abnormal, and turning to the step 5.7;
step 5.6, if list [ T ] work ]Less than V end From list [ T work ]To list [ T ] work -1]Direction and completion threshold V end Comparing one by one until the current value is larger than V end Indicating completion and recording completion time; if list [ T ] work ]Greater than V end From list [ T work ]To list [ T ] work +1]Direction and completion threshold V end Comparing one by one until the current value is less than V end Completing the record; if list [ T ] work ]To list [ T ] work +T wit ]All values in are greater than V end With T work +T wit Deleting all data in the list by taking the corresponding moment as the completion time, and turning to the step 7.8;
step 5.7, judging the total current of the section of data, if the total current is larger than f 1V totl From list [ T work ]To list [ T ] work +1]Direction and completion threshold V end Comparing one by one, and synchronizing the step 5.6; if less than f 2V totl And is greater than f 3V totl From list [ T work ]To list [ T ] work -1]Direction and completion threshold V end Comparing one by one, and if the threshold interval is not met, indicating abnormal fluctuation in the standby state, going to the step 5.2;
and 5.8, recording the start time, the completion time and the production abnormal mark of the part, and ending the algorithm.
The threshold algorithm for distinguishing the device processing state from the standby state in step 3.1 is specifically as follows:
taking a segment { p '}, { p' } in the current data sequence { p } which at least comprises 5 complete part machining processes, finding the positions of the maximum value and the minimum value, searching the minimum value x1 in the left neighborhood of the maximum value and the minimum value x2 in the right neighborhood, and making x be mx { x1, x2}, and similarly, searching the maximum values z1, z2 in the minimum value neighborhood and making z be min { z1, z2 }. X and z respectively represent that the current in the machining state fluctuates up and down around x and the current in the standby state fluctuates up and down around z, and the values of x and z are adjusted to obtain a start-up threshold value V strt Completion threshold V end 。
The algorithm of the standby threshold for distinguishing the tool changing state and the feeding and discharging state in the step 3.2 is as follows:
according to the start-up threshold V strt And completion threshold V end Calculating all standby Time { Time } in the current data sequence { p' }, including loading and unloading, tool changing and abnormal fluctuation Time, as follows: traversing the data { p' }, and judging whether the current value is less than V or not end If yes, start to count num + + until the current value is larger than V strt The num value is stored in Time'. Removing abnormal values according to actual conditions and calculating standby threshold T in a classified mode wit (ii) a If there is a tool change, then T wit Taking m between the time of feeding and discharging and tool changing, if no tool changing, T wit Taking the value n, wherein n is larger than 0 and smaller than the minimum value of { Time' }.
The data segmentation method in step 3.3 is specifically as follows:
according to the start-up threshold V strt Completion threshold V end Standby threshold T wit Segmenting the collected current data, calculating the following steps, traversing the data { p }, calculating by using the method in the step 6.2 to obtain a standby Time set { Time }, a standby starting point set { pws }, a standby ending point set { pwe }, and deleting less than T in the { Time } wit And adjusting the corresponding relation of the sets according to the data in the (pws) and the (pwe) data, namely obtaining the processing starting point and the processing ending point of all the middle parts, and realizing segmentation.
Taking the example of processing a certain shaft part 1 by a certain device, the current is collected by the sensing module 1.1, converted into a digital signal by the a/D conversion module 1.2, and transmitted to the data storage module 1.3 for storage, and fig. 7 is a current data curve of the part 1, which includes 8 complete production processes. And downloading data in the computer, calibrating, and uploading a calibration result to a process characteristic database in the server. Table 1 shows calibrated process feature vectors, the process features are searched in a database and process feature codes are generated, a start-up code scanning module 1.4 scans the process feature codes and production plan command codes, and transmits information to a main control monitoring module 1.5 to monitor the production state of the equipment, table 2 shows real-time monitoring results of the parts 1, start-up time and completion time of each part are displayed on a screen display module 1.7, and meanwhile, the processing time of each part is calculated and displayed and is about 77s, and the standby time of the equipment is long, and the time is unequal due to manual loading and unloading. And finally, uploading the start-up time and the completion time to a production record database in the server for a management end to call.
Taking the example of processing a certain shaft part 2 by a certain device, the part has a tool changing state compared with the part 1, and fig. 8 is a current data curve of the part 2, which comprises 8 complete production processes. Table 3 is the calibrated process feature vector and table 4 is the partial real-time monitoring result. It can be seen that the processing time 26s of the 5 th workpiece of the part 2 is obviously shorter than the average processing time 33s of the first 4 parts, the processing time in the production abnormity mark is too short, at this time, the buzzer of the abnormity alarm module 1.7 starts to give an alarm, and finally, an abnormity mark exists in the uploaded production record, so that a supervisor is prompted to carry out processing and follow-up information tracing of quality problems.
TABLE 1 Process characterization parameters obtained by calibration of part 1 for a certain process
TABLE 2 5 production records obtained by real-time monitoring of a certain process of part 1
TABLE 3 Process characterization parameters obtained by calibrating a certain process for part 2
TABLE 4 5 production record information obtained by real-time monitoring of certain process of part 2
It should be noted that the terms "upper", "lower", "left", "right", "front", "back", etc. used in the present invention are for clarity of description only, and are not intended to limit the scope of the present invention, and the relative relationship between the terms and the terms is not limited by the technical contents of the essential changes.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.
Claims (10)
1. The equipment production state monitoring system is characterized by comprising an equipment production state monitor (1), a factory local area network, a client computer and a server computer, wherein the equipment production state monitor (1) is communicated with the client computer and the server computer through the factory local area network; the equipment production state monitor comprises a current sensing module (1.1), an A/D conversion module (1.2), a data storage module (1.3), a start-up code scanning module (1.4), a main control monitoring module (1.5), a touch display screen (1.6), an abnormity alarm module (1.7) and a communication transmission module (1.8);
the current sensing module (1.1) is connected with the main control monitoring module (1.5) through the A/D conversion module (1.2), converts the acquired current analog signal into a digital signal and transmits the digital signal to the main control monitoring module (1.5);
the data storage module (1.3) is connected with the main control monitoring module (1.5) through an ST interface and is responsible for storing current digital signals obtained after A/D conversion;
the start-up code scanning module (1.4) is connected with the master control monitoring module (1.5) through a USB interface and is responsible for scanning the process characteristic two-dimensional code and the production plan two-dimensional code, acquiring process characteristic information and production plan information and transmitting the acquired information to the master control monitoring module (1.5);
the touch display screen (1.6) is connected with the main control monitoring module (1.5) through an HDMI interface, and is responsible for displaying real-time monitoring result information of the main control monitoring module (1.5) and receiving a working instruction given by a user through a touch control mode;
the abnormity alarm module (1.7) is connected with the main control monitoring module (1.5) through an RS485 interface, and when the abnormal production information is monitored, a buzzer is used for carrying out sound alarm;
the main control monitoring module (1.5) uploads the monitored equipment production information to a server computer connected with a factory local area network through a communication transmission module (1.8).
2. A system for monitoring the production state of a plant according to claim 1, characterized in that the server computer runs a database system (2.3) comprising a process characteristics database (2.3.1), a production planning database (2.3.2) and a production record database (2.3.3).
3. A system for monitoring the production state of a plant according to claim 2, characterized in that each record of the process signature database (2.3.1) comprises 1 process signature and 6 signature parameters: threshold value V for start-up strt Completion threshold V end Standby threshold T wit Machining period T work Standard current and V totl Standard mean current V vg Wherein the process feature code is a unique primary key field; each record of the production plan database (2.3.2) comprises 3 fields of a production plan order number, a process name and a plan number, wherein the production order number is a unique main key field; the production record database (2.3.3) records the production record information of each part obtained by the real-time monitoring of the equipment production state monitor 1, and comprises 4 fields of a production plan order number, a machining starting time, a machining ending time and a production abnormity mark.
4. A system for monitoring the production state of a device according to claim 1, characterised in that the management software (2.2) running on the client computer comprises the following functions: downloading a current data file from the equipment production state monitor (1), inquiring the state information of the equipment monitored by the equipment production state monitor (1), calibrating the process characteristics according to a production process characteristic calibration algorithm, storing the calibrated process characteristics into a process characteristic database, inquiring information in a database system (2.3), printing a process characteristic two-dimensional code, and printing a production plan two-dimensional code.
5. A working method of a device production state monitoring system is characterized by comprising the following steps:
step 1, the equipment production state monitor (1) is powered on and started up, and the selection of the working mode is carried out through the touch display screen (1.6), and the equipment production state monitor comprises two working modes: calibrating a data acquisition mode and a real-time production monitoring mode;
step 2, carrying out a calibration data acquisition working mode, comprising two working instructions: the method comprises the steps that a calibration data acquisition instruction is started, the calibration data acquisition instruction is finished, after the calibration data acquisition instruction is started to be issued, a main control monitoring module (1.5) stores a current digital signal transmitted by an A/D conversion module (1.2) to a data storage module (1.3), during storage, the time of starting acquisition is used as the file name of a current data file, and after the calibration data acquisition instruction is finished to be issued, acquisition and data storage are stopped;
step 3, a user downloads an obtained current data file by using management software (2.2) running on a client computer through a communication transmission module (1.8) of the equipment production state monitor (1), the management system (2.2) calibrates the characteristics of the production process by using a production process characteristic calibration algorithm according to the current data file, the management system (2.2) stores the calibration result of the process characteristics into a process characteristic database (2.3.1), each production process characteristic record is identified by using a unique process characteristic code, and an increasing sequence digital code is used as a process characteristic code;
step 4, carrying out a real-time production monitoring working mode of the equipment production state monitor (1), wherein the working mode comprises two stages of code scanning start-up and real-time monitoring, in the code scanning start-up stage, a start-up code scanning module (1.4) sequentially scans a production plan two-dimensional code and a process characteristic two-dimensional code, transmits process characteristic information and production plan information obtained by code scanning to a main control monitoring module (1.5), and then enters a real-time monitoring stage;
step 5, in the real-time monitoring stage of the real-time production monitoring working mode of the equipment production state monitor (1), the main control monitoring module (1.5) stores the current digital signal transmitted by the A/D conversion module (1.2) into the data storage module (1.3), and performs real-time analysis by using process characteristic information and production plan information and an equipment real-time monitoring algorithm to obtain equipment operation information, wherein the equipment operation information comprises equipment state information and equipment production information, and the equipment state information comprises a state type and a state occurrence moment; the equipment production information comprises a production plan order code, machining starting time, machining ending time and a production abnormal mark of the part; the device state types include three states: stopping, standby and processing; production anomaly markers include four markers: too long processing time, too short processing time, too large processing current and too small processing current.
Step 6, in the real-time monitoring stage of the equipment production state monitor (1) in the real-time production monitoring working mode, the main control monitoring module (1.5) performs production reporting and abnormal alarming according to the equipment operation information obtained through real-time analysis, wherein the production reporting refers to a process that the main control monitoring module (1.5) uploads the monitored equipment production information to a production record database (2.3.3) on a server computer through a communication transmission module (1.8) in the real-time production monitoring working mode; the abnormal alarm is as follows: judging whether the monitored equipment production information has production abnormity, if the production abnormity exists, carrying out sound alarm through an abnormity alarm module (1.7) to prompt a user that the equipment production is abnormal;
and 7, in the real-time monitoring stage of the equipment production state monitor (1) in the real-time production monitoring working mode, the main control monitoring module (1.5) displays equipment state information on a touch display screen (1.6) according to the equipment running information obtained by real-time analysis, wherein the equipment state information comprises three equipment state types including shutdown, standby and processing and state query time.
6. The method of claim 5, wherein the step of the calibration algorithm for the characteristics of the manufacturing process in step 3 comprises the following steps:
step 3.1, aiming at the standby state and the processing state of the equipment, calculating a start threshold V of the processing state of the equipment by adopting a threshold algorithm for distinguishing the processing state and the standby state of the equipment strt Completion threshold V end ；
Step 3.2, aiming at the two states of loading and unloading and tool changing existing in the standby state of the equipment, a standby threshold value algorithm for distinguishing the tool changing state and the loading and unloading state is adopted to calculate a standby threshold value T wit ；
Step 3.3, according to the start-up threshold V strt Completion threshold V end Standby threshold T wit Segmenting the collected current data, wherein each segment of data represents a complete part machining process, calculating the machining time of each part according to the machining starting point and the machining ending point of the part obtained after segmentation, removing abnormal values, and taking the median as the machining period T of the part work (ii) a Accumulating and summing currents in the processing time of each part to obtain a current sum, dividing the current sum by the number of sampling points to obtain a mean current, removing an abnormal value, and taking a median to obtain a standard current sum V totl And standard mean current V vg ；
Step 3.4, 6 production characteristic parameters V obtained by calculation in the step 3.1 to the step 3.3 strt 、V end 、T wit 、T work 、V totl 、V vg And uploading the data to a process characteristic database (2.3.1) in a server computer.
7. The working method of the equipment production state monitoring system according to claim 5, wherein the real-time equipment monitoring algorithm in the step 5 comprises the following steps:
step 5.1, according to the production process characteristic parameter V obtained by the start-up code scanning module 1.4 strt 、V end 、T wit 、T work 、V totl 、V vg Setting a standard average current V vg Tolerance V, standard current and V totl Value coefficients f1, f2, f3, striker threshold V H ；
Step 5.2, the main control monitoring module (1.5) additionally stores the real-time acquired equipment current value into an array list, and the step 5.3 is carried out;
step 5.3, if the current value is greater than the tool bumping threshold value V H The method comprises the following steps of (1) indicating that a tool bumping accident occurs in the production process of equipment, giving a production abnormal identifier of large machining current by an algorithm, and indicating the shutdown state of the equipment if the current value is equal to 0; if the current value is increased rapidly from 0 and tends to be stable, the equipment is started, and the starting time is recorded by an algorithm; if the current is changed from the large value to 0, the equipment is shut down, the algorithm records the shutdown time, and the step 5.4 is carried out;
step 5.4, if the number of the collected data points is less than T work +T wit Go to step 5.2; if the number of the collected data points is more than or equal to T work +T wit If so, then pair with the last previous T work Analyzing the data, and judging the start-up threshold V strt Whether the size is between the first element list [0 ] of the data segment]With the adjacent element list [1 ]]If yes, it indicates the start of work, and list [1 ]]Taking the corresponding moment as the start time and recording, turning to the step 5.5, otherwise, deleting the first element list [0 ]]Go to step 5.2;
step 5.5, judging T before list work Whether the average value of the individual data is in the interval [ V ] vg -v，V vg +v]If yes, the processing is normal, and the step 5.6 is carried out; otherwise, indicating that the processing is abnormal, and turning to the step 5.7;
step 5.6, if list [ T ] work ]Less than V end From list [ T work ]To list [ T ] work -1]Direction and completion threshold V end Comparing one by one until the current value is larger than V end Indicating completion and recording completion time; if list [ T ] work ]Greater than V end From list [ T work ]To list [ T ] work +1]Direction and completion threshold V end Comparing one by one until the current value is less than V end Completing the record; if list [ T ] work ]To list [ T ] work +T wit ]All values in are greater than V end With T work +T wit Deleting all data in the list by taking the corresponding moment as the completion time, and turning to the step 5.8;
and 5.7, judging the total current of the section of data, and if the total current is greater than f 1V totl From list [ T work ]To list [ T ] work +1]Direction and completion threshold V end Comparing one by one, and synchronizing the steps 5.6; if less than f 2V totl And is greater than f 3V totl From list [ T work ]To list [ T ] work -1]Direction and completion threshold V end Comparing one by one, and if the threshold interval is not met, indicating abnormal fluctuation in the standby state, going to the step 5.2;
and 5.8, recording the start time, the completion time and the production abnormal mark of the part, and ending the algorithm.
8. The method for operating a system for monitoring the production status of a device according to claim 5, wherein the threshold algorithm for distinguishing the processing status from the standby status of the device in step 3.1 is as follows:
taking a segment { p '}, { p' } in the current data sequence { p } which at least comprises 5 complete part machining processes, finding the positions of the maximum value and the minimum value, searching the minimum value x1 in the left neighborhood of the maximum value and the minimum value x2 in the right neighborhood, and making x be mx { x1, x2}, and similarly, searching the maximum values z1, z2 in the minimum value neighborhood and making z be min { z1, z2 }. X and z respectively represent that the current in the machining state fluctuates around x and the current in the standby state fluctuates around z, and the values of x and z are adjusted to obtain a start-up threshold value V strt Completion threshold V end 。
9. The working method of the equipment production state monitoring system according to claim 5, wherein the algorithm of the standby threshold for distinguishing the tool changing state from the loading and unloading state in the step 3.2 is as follows:
according to the start-up threshold V strt And completion threshold V end Calculating all standby Time { Time } in the current data sequence { p' }, including loading and unloading, tool changing and abnormal fluctuation Time, as follows: traversing the data { p' }, and judging whether the current value is less than V or not end If yes, start to count num + + until the current value is larger than V strt The num value is stored in Time'. Removing abnormal values according to actual conditions and calculating standby threshold T in a classified mode wit (ii) a If there is a tool change, then T wit Taking m between the time of feeding and discharging and the time of tool changing, if no tool changing exists, T wit Taking the value n, wherein n is larger than 0 and smaller than the minimum value of { Time' }.
10. The operating method of the equipment production state monitoring system according to claim 5, wherein the data segmentation method in step 3.3 is as follows:
according to the start-up threshold V strt Completion threshold V end Standby threshold T wit Segmenting the collected current data, calculating the following steps, traversing the data { p }, calculating by using the method in the step 3.2 to obtain a standby Time set { Time }, a standby starting point set { pws }, a standby ending point set { pwe }, and deleting less than T in the { Time }, wherein the number of T is less than T in the standby starting point set { Time } wit And adjusting the corresponding relation of the sets according to the data in the (pws) and the (pwe) data, namely obtaining the processing starting point and the processing ending point of all the middle parts, and realizing segmentation.
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