CN117313009A - Fault prevention method based on machining center equipment data - Google Patents
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Abstract
The invention discloses a fault prevention method based on machining center equipment data, which comprises the following steps: acquiring processing data of processing center equipment, storing the processing data into a database, cleaning the acquired data, and removing abnormal values; extracting the characteristic value of a normal working machine type, grouping the data, solving the root mean square, extracting the skewness and kurtosis, and constructing a prevention model; and monitoring the characteristic value through the set upper limit and lower limit, and verifying the prevention model and the reverse normal state. According to the invention, a model is built through data analysis, so that the damage trend of a screw rod, a guide rail and a sliding block of processing center equipment can be accurately predicted; the working efficiency and the accuracy of equipment spot inspection are improved, and faults caused by burst are eliminated.
Description
Technical Field
The invention belongs to the technical field of machining, and particularly relates to a method for predicting faults based on machining center equipment data analysis.
Background
The machining center equipment is used as core equipment in the field of machining, and the precision and faults of the equipment have direct influence on the normal development of production. The faults of the X\Y\Z axis screw rod, the guide rail and the sliding block of the processing center equipment can generate a large number of poor engineering and can cause the large fault shutdown of the production line. The problem has three characteristics of gradual degeneration, concealment and serious consequences, namely the damage process of the screw guide rail sliding block is gradual degradation, the degradation process is hidden and is not easy to find, and the problems of production and spot inspection maintenance are caused. The conventional construction can only be ascertained by manually checking the TPM, and mainly is performed by measuring, touching, operating, visual and other methods. In view of the effect of long-term execution, although the construction method prevents some faults in advance, problems are also exposed in execution, and in normal production, the equipment is frequently subjected to the phenomena of fault shutdown, poor machining and the like caused by damage of guide rails, lead screws and sliding blocks due to the long time consumption of point inspection, low point inspection accuracy and the like.
Disclosure of Invention
Aiming at the problems existing in the background technology, the invention aims to provide a fault prevention method based on machining center equipment data, which utilizes analysis tools such as a computer to cooperatively analyze and model load, rotation speed, feeding and other parameter data and plays a role in preventing damage faults of a machining center screw rod and a guide rail sliding block.
In order to achieve the above purpose, the invention designs a fault prevention method based on machining center equipment data: acquiring processing data of processing center equipment, storing the processing data into a database, cleaning the acquired data, and removing abnormal values; extracting the characteristic value of a normal working machine type, grouping the data, solving the root mean square, extracting the skewness and kurtosis, and constructing a prevention model; and monitoring the characteristic value through the set upper limit and lower limit, and verifying the prevention model and the reverse normal state.
Preferably, the processing data includes: the state, yield, program, multiplying power, cutter, load, coordinates and rotating speed of the machining center equipment.
Preferably, the outliers are identified by using the box-line graph model, and the outliers are cleaned as outliers.
Preferably, the data generated during the movement of the device is load data of the device to be extracted, and whether the device is moving is judged by comparing the difference value between the acquired data and the adjacent last piece of data.
Further preferably, the criteria for determining whether the device is moving are: and (5) judging that the equipment is not moved when the movement distance of each axis of the equipment is less than 0.01mm within 200ms, and rejecting the data.
Preferably, the root mean square is calculated as:
wherein: n is the sample size, x i Is the i-th measurement.
Preferably, the calculation formulas of skewness and kurtosis are as follows:
degree of deviation
Wherein: e is the expectation operator, X is the random variable, μ is the mean, δ is the standard deviation, μ 3 Is the third order active difference. The invention uses the third-order accumulated quantity k 3 And second order cumulative amount k 2 To 1.5 th power of (2) to represent the skewness gamma 1 。
Kurtosis degree
Wherein: k (K) 4 Is the center moment of a fourth-order sample, K 2 Is the second order central moment, mu 4 Is the fourth order active difference, delta is the standard deviation, n is the sample size, x i Is the i-th measurement value, and,is the average of the samples.
The beneficial effects of the invention are as follows: the trend of damage of the screw rod, the guide rail and the sliding block of the machining center equipment can be accurately predicted by establishing a model through data analysis; the working efficiency and the accuracy of equipment spot inspection are improved, and faults caused by burst are eliminated. And the faults caused by the damage of the lead screw and the guide rail slide block can be predicted in advance through the data analysis of the processing center.
Drawings
FIG. 1 is a box diagram model of the present invention;
FIG. 2 is a normal curve of a preventive model of the invention;
FIG. 3 is a graph of yet more outliers;
FIG. 4 is an OP30-6 load data model monitoring graph;
FIG. 5 is an OP20-7 data model monitoring graph;
FIG. 6 is an OP20-10 data model monitoring graph.
Detailed Description
The following describes the invention in further detail, including preferred embodiments, by way of the accompanying drawings and by way of examples of some alternative embodiments of the invention. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to fall within the scope of the invention.
As shown in fig. 1 to 6, the fault prevention method based on the machining center equipment data, which is designed by the invention, constructs the network communication of the workshop machining center, collects and stores parameters of the machining equipment into a database through an edge collection platform, cleans the data by using an analysis tool, and completes monitoring modeling and fault prevention. The method specifically comprises the following steps:
s1, network architecture: and constructing a local area network, wherein each processing center accesses the server and the data center through the Ethernet, and the host accesses the database through the Ethernet. The communication protocol may be FOCUS, MTCONNECT, MC or the like as required.
S2, data acquisition: the data are collected and stored in a database, and the collection frequency is less than 200ms. The collected data types include: the state, yield, program, multiplying power, cutter, load, coordinates, rotation speed, etc. of the machining center equipment.
Status of the device: for confirming the real-time status of the device, e.g. whether the device is running, stopped, idle or in a debug state. By monitoring the different data analysis tasks, the corresponding device status data can be analyzed. The state of the device includes:
operation (processing): the device operates in the machining program in an automatic mode;
idle (waiting): the device waits for program start in an automatic mode;
debugging (manual): the device is in a manual debug mode;
alarm (stop): the equipment system generates fault alarm (part of alarm priority of the equipment is lower than the running state and higher than the idle and debugging states);
shutdown (offline): the equipment system is powered off and the network communication is interrupted.
Yield: recording the output condition of the equipment, and slicing the data with the output of a single equipment as a period. The yield is the number of the equipment and the parts to be accumulated; the part yield count would be increased by 1 per 1 part processed.
The procedure is as follows: refers to the program name of the different parts processed by the equipment. Different parts require different machining programs including machining paths, tool sequences, machining parameters, etc. The data of the acquisition program can be used to trace back the detailed process and parameters of the part machining.
Multiplying power: the device comprises a feeding multiplying power and a main shaft multiplying power and is used for confirming whether the device is processed according to preset parameters. It is one of the rules of data cleaning, and the data (feed rate and spindle rate are 100%) during normal processing of the equipment is selected for subsequent analysis so as to eliminate the interference of abnormal data.
Feed rate: the machining center feed rate is the ratio of the relative movement distance between the workpiece and the tool to the machine tool feed rate when the machining center performs machining, and the greater the feed rate, the longer the distance the workpiece moves relative to the tool, and the faster the machining speed. The rate of feed is given by machine parameters such as 100%, 50%, 25% and 0. 100% at the time of the positive working.
Spindle magnification: is the ratio between the spindle rotation speed and the original set speed. In machine tool processing, different materials, different cutters and different processes require different rotational speeds, and the spindle magnification of the machine tool can meet different processing requirements by adjusting the rotational speed of the spindle. As with feed rate, the rate of spindle is given by machine parameters, such as 100%, 50%, 25% and 0. 100% for normal processing.
Cutting tool: the use sequence of the cutters and the serial number information of the cutters are recorded and are used for analyzing the processing states of different cutters.
Load: load data of a main shaft, an X axis, a Y axis, a Z axis, a B axis and the like are collected and used for monitoring and evaluating the running state of each axis of equipment. And detecting abnormal conditions in time so as to prevent overload and faults of equipment.
During the processing of the processing center, the load of each shaft can be affected to different degrees. The load of each shaft is precisely controlled by digital signals given by a CNC system. For example: p=20 in the machining center generally represents the work load of a workpiece when machined on a machine tool, where P represents the work load in percent and 20 represents the work load of 20% +|! Typically, the normal operating load ranges for each axis of a CNC machining center are as follows:
(1) spindle load: the spindle takes the greatest load. The normal load range is 20% -80%. If the load exceeds 80%, it is necessary to check whether the adjustment of the spindle and the cutting conditions are reasonable.
(2) Feed shaft load: the normal operation load range of the feed shaft is 15% -35%. If this range is exceeded, it is necessary to check whether the cutting speed and cutting condition of the feed shaft are reasonable.
(3) Cutting feed shaft load: the load of the cutting feed shaft is generally between 30% and 80%, and if this range is exceeded, it is necessary to check whether the cutting conditions of the feed shaft (tool) are reasonable.
(4) Rotation shaft load: the load on the rotating shaft is typically between 10% and 50%. If it is out of this range, it is necessary to check whether the adjustment of the rotation shaft and the cutting condition are reasonable.
Coordinates: the machining center coordinate system refers to a coordinate system used to describe a workpiece in a numerical control machine or machining center. It is a three-dimensional coordinate system, usually represented using a rectangular coordinate system, including an X-axis, a Y-axis, and a Z-axis. Units: millimeter.
And recording position information of the equipment in the processing process, wherein the position information comprises X-axis, Y-axis, Z-axis and B-axis coordinate data. This is another rule for data cleansing, which ensures the accuracy and validity of the data by culling out non-processed data when not moving.
Spindle rotational speed: spindle speed is an important parameter in the field of mechanical equipment, which refers to the number of revolutions of the spindle per unit time. Its unit is typically revolutions per minute (r/min). The method is used for monitoring the running state of the main shaft and distinguishing the start-stop state of the main shaft during processing.
S3, data processing: identifying an outlier by using the box line graph model, and cleaning the outlier; and meanwhile, redundant data of the machining center equipment are cleaned, including state data of the machining center equipment in shutdown, faults and the like.
As shown in fig. 1, an example of the box diagram model of the present invention is given in which the median is the median of the dataset, that is, the 50% number after all values in the dataset are arranged from small to large; the first quartile (Q1) is the 25 th number after all values in the dataset are arranged from small to large; the third quartile (Q3) is the 75% number after all values in the dataset are arranged from small to large; the quartile range (IQR) is the difference between the third quartile and the first quartile.
Lower boundary=q1-1.5×iqr, upper boundary=q3+1.5×iqr.
When the difference between the value and the first quartile and the third quartile is above 1.5 xIQR, the value is an outlier. That is, the outlier is to the left of the lower boundary or to the right of the upper boundary in the graph. Values with a gap above 3×iqr are called extreme outliers.
S4, data analysis and fault prevention: analyzing the data in S3 based on the Pandas library in the Python language, including: and establishing a single equipment model and comparing and analyzing the same procedure.
Establishing a single equipment model:
the idea of model construction is to construct the upper and lower limits based on the 3 delta standard deviation for normal distribution data.
Firstly, data preparation is needed, acquisition data is continuously acquired in real time, no situation of acquiring a blank value exists in the acquisition process, therefore, no situation of a missing value exists, the researched numerical value is equipment load data, equipment types are simple and unified, the difference of normal operation extremum is small, data conversion and reduction are not needed, only data cleaning is needed, the load data of equipment only change when the equipment moves, the changed part is needed, but acquisition data is continuously acquired in real time, so that data which are not changed continuously are acquired when the equipment does not move, whether the equipment moves is judged by comparing the difference value of the acquisition data and the adjacent last piece of data, the judgment standard is that the equipment does not move within 200ms, and the data is removed in a non-production mode, for example, the equipment debugging state, the manual mode and the data in a fault mode are all cleaned and removed;
secondly, after data preparation is completed, taking a single-cycle load average value as a data unit, sequencing and smoothing all the data units in a section according to a time axis, carrying out data smoothing on the sliding average value by selecting 30 unit sliding windows, still meeting normal distribution according to central limit theorem data, solving standard deviation by using the smoothed data series, constructing upper and lower limits according to a rule of 3 delta standard deviation, completing construction of a monitoring model of a single device, and identifying the condition that the single value exceeds the upper and lower limits as abnormal.
As shown in fig. 2, wherein: the upper and lower dashed lines are the upper and lower limits of the 3 delta standard deviation; gray lines are load averages for a single cycle; the black solid line is the result of selecting 30 unit sliding windows to carry out sliding average on the blue line; the horizontal axis represents the number of cycles.
The grouping of the data is carried out according to the data collected by processing a single workpiece, the purpose of the grouping is to ensure that the data of each data unit is relatively uniform, namely, the data are all complete processing cycles, and the data after the grouping become the data units constructed by a subsequent model, namely, the basic data constructed by the subsequent model.
The average value of a single feature is extracted as the feature, a sliding window is established for data set construction, the extracted feature meets normal distribution according to the central limit theorem, the feature value is monitored and managed by setting the upper limit and the lower limit through the 3 delta feature, and the equipment with more contact limit is suspected to be abnormal equipment, namely point inspection and analysis are carried out.
And then analyzing and running the data units of the load mean values of the single cycles of different devices in the same procedure, eliminating positive and negative values by using a root mean square calculation method, comparing the positive and negative values with the devices in the same procedure, solving the skewness and kurtosis of the data units, constructing a curve, constructing a model for difference comparison among the different devices in the same procedure, intuitively finding out the relative difference of each device according to the root mean square, finding out the normal distribution of the data according to the skewness and kurtosis, and understanding the influence of other factors if the deviation is larger, so that the skewness and kurtosis deviate too much from the normal distribution, thereby judging that the device is in abnormal state operation.
And (3) comparing and analyzing among the same procedures:
the method comprises the steps of analyzing and running data units of load mean values of single cycles of different equipment, eliminating positive and negative values by using a root mean square calculation method, comparing the positive and negative values with equipment in the same procedure, solving the skewness and kurtosis of the data units, constructing a curve, constructing a model for difference comparison among different equipment in the same procedure, intuitively finding out the relative difference of each equipment according to root mean square, finding out the normal distribution of the data according to the skewness and kurtosis, and judging that the equipment works in an abnormal state due to the fact that the skewness and kurtosis deviate too much from the normal distribution if the influence of other factors is larger due to larger deviation.
And extracting the model characteristics of the data to be used, grouping the group by group according to the processing station, solving the root mean square, extracting the related data of skewness and kurtosis, comparing the related data of the skewness with the same equipment, and carrying out normal verification. Wherein,
root mean square
Wherein: n is the sample size, x i Is the i-th measurement.
Degree of deviation
Wherein: e is the expectation operator, X is the random variable, μ is the mean, δ is the standard deviation, μ 3 Is the third order active difference. The invention uses the third-order accumulated quantity k 3 And second order cumulative amount k 2 To 1.5 th power of (2) to represent the skewness gamma 1 。
Kurtosis degree
Wherein: k (K) 4 Is the center moment of a fourth-order sample, K 2 Is the second order central moment, mu 4 Is the fourth order active difference, delta is the standard deviation, n is the sample size, x i Is the i-th measurement value, and,is the average of the samples.
Example 1
Load data model monitoring finds anomalies: OP30-6.
Performing spot check on equipment with abnormal data analysis: and (3) checking whether the guide rail, the sliding block and the lead screw are damaged and severely worn, and confirming the precision and lubrication spot checking of each shaft of the equipment. Inspection shows that (1) the precision is out of tolerance; (2) the slider is damaged.
| Project | Standard of | Results |
| Near end (reference) | ≤0.01 | 0 |
| Distal end | ≤0.02 | 0.015 |
| Upper bus | ≤0.015 | 0.06 (out of tolerance) |
| Side bus | ≤0.015 | 0.013 |
| Lubrication | Normal oil application | OK |
Actual spot inspection results: the Y axis has abnormal sound and vibration, and the X axis sliding block is damaged; the lubrication inside the equipment is normal.
As shown in fig. 4, the data analysis results: there is an anomaly in the Y-axis data.
Overall results: and the data analysis is consistent with the actual spot inspection result.
Example two
Load data model monitoring finds anomalies: the Z-axis data of OP20-7 and OP20-10 are abnormal.
OP20-7 precision results
| Project | Standard of | Results |
| Near end (reference) | ≤0.01 | 0 |
| Distal end | ≤0.02 | 0 |
| Upper bus | ≤0.015 | 0.02 (out of tolerance) |
| Side bus | ≤0.015 | 0.02 (out of tolerance) |
| Lubrication | Normal oil application | Normal oil supply |
OP20-10 precision results
| Project | Standard of | Results |
| Near end (reference) | ≤0.01 | 0 |
| Distal end | ≤0.02 | 0.005 |
| Upper bus | ≤0.015 | 0.02 (out of tolerance) |
| Side bus | ≤0.015 | 0.05 |
| Lubrication | Normal oil application | Normal oil supply |
Actual spot inspection results:
OP20-7: and checking the equipment precision, the guide rail, the sliding block and the screw rod normally.
Abnormal problem: z-axis sliding block aluminum scraps are piled up, and countermeasure: cleaning aluminum scraps
OP20-10: the Z-axis sliding block is damaged, and the balls fall off the inner groove of the guide rail, so that poor machining quality of the quality parts is easily caused.
Overall results: the monitoring graph adopting the method of the invention is shown in fig. 5 and 6, and the data analysis is consistent with the actual spot inspection result.
With the continuous operation of the model, the abnormal condition during the operation is successfully found, the real object confirmation is carried out, the gradual investigation is carried out on the existing processing center in accordance with the model prediction, the point inspection confirmation is carried out on the existing objection equipment, and the fault finding accuracy rate reaches more than 90 percent
It will be readily understood by those skilled in the art that the foregoing is merely illustrative of the preferred embodiments of the present invention and that various modifications, combinations, substitutions, improvements, etc. may be made without departing from the spirit and principles of the invention.
Claims (8)
1. A method for preventing faults based on machining center equipment data, comprising the steps of:
s1, data acquisition: acquiring processing data of processing center equipment, and storing the processing data into a database;
s2, data cleaning: cleaning the collected data and removing abnormal values;
s3, constructing a prevention model:
s301, extracting device load data of a normal working machine type,
s202, grouping data;
s303, obtaining root mean square of the grouped data;
s304, extracting skewness and kurtosis to construct a prevention model;
s4, preventing faults: and monitoring the characteristic value through the set upper limit and lower limit, and carrying out normal verification on the characteristic value and the prevention model.
2. The failure prevention method based on machining center equipment data according to claim 1, characterized in that: in the step S1, the processing data includes: the state, yield, program, multiplying power, cutter, load, coordinates and rotating speed of the machining center equipment.
3. The failure prevention method based on machining center equipment data according to claim 1, characterized in that: in step S2, an outlier is identified by using the box map model, and the outlier is cleaned as an outlier.
4. The failure prevention method based on machining center equipment data according to claim 1, characterized in that: in step S301, the data generated during the movement of the device is the load data of the device to be extracted, and whether the device is moving is determined by comparing the difference between the collected data and the previous piece of data.
5. The method for preventing faults based on machining center equipment data of claim 4, wherein: the criteria for the determination of whether the device is moving are: and (5) judging that the equipment is not moved when the movement distance of each axis of the equipment is less than 0.01mm within 200ms, and rejecting the data.
6. The failure prevention method based on machining center equipment data according to claim 1, characterized in that: in step S302, the data collected by processing a single workpiece are grouped, so that the data composition of each data unit is relatively uniform, i.e. the data is a complete processing cycle.
7. The failure prevention method based on machining center equipment data according to claim 1, characterized in that: in step S303, the root mean square formula is:
wherein: n is the sample size, x i Is the i-th measurement.
8. The failure prevention method based on machining center equipment data according to claim 1, characterized in that: in step S304, the calculation formulas of the skewness and kurtosis are as follows:
degree of deviation
Wherein: e is the expectation operator, X is the random variable, μ is the mean, δ is the standard deviation, μ 3 Is the third order active difference. The invention uses the third-order accumulated quantity k 3 And second order cumulative amount k 2 To 1.5 th power of (2) to represent the skewness gamma 1 。
Kurtosis degree
Wherein: k (K) 4 Is the center moment of a fourth-order sample, K 2 Is the second order central moment, mu 4 Is the fourth order active difference, delta is the standard deviation, n is the sample size, x i Is the i-th measurement value, and,is the average of the samples.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117789999A (en) * | 2024-02-27 | 2024-03-29 | 济宁医学院附属医院 | Medical health big data optimization acquisition method |
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| CN117789999A (en) * | 2024-02-27 | 2024-03-29 | 济宁医学院附属医院 | Medical health big data optimization acquisition method |
| CN117789999B (en) * | 2024-02-27 | 2024-05-03 | 济宁医学院附属医院 | Medical health big data optimization acquisition method |
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