CN114459523A - Calibration early warning method for online quality detection instrument - Google Patents
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- 238000001514 detection method Methods 0.000 title claims abstract description 86
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- 238000005259 measurement Methods 0.000 claims description 72
- 235000019504 cigarettes Nutrition 0.000 claims description 18
- 238000007689 inspection Methods 0.000 claims description 9
- 238000012360 testing method Methods 0.000 claims description 5
- 238000005070 sampling Methods 0.000 claims description 3
- 238000003860 storage Methods 0.000 claims description 2
- 238000004364 calculation method Methods 0.000 abstract description 6
- 230000000694 effects Effects 0.000 description 9
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- 238000004519 manufacturing process Methods 0.000 description 5
- 238000009423 ventilation Methods 0.000 description 3
- 238000012790 confirmation Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
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- 238000009826 distribution Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
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- 230000005856 abnormality Effects 0.000 description 1
- 238000004164 analytical calibration Methods 0.000 description 1
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- 238000013479 data entry Methods 0.000 description 1
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- 238000012544 monitoring process Methods 0.000 description 1
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- G—PHYSICS
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- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D18/00—Testing or calibrating apparatus or arrangements provided for in groups G01D1/00 - G01D15/00
Abstract
The invention discloses a calibration early warning method of an online quality detection instrument, belonging to the technical field of detection equipment calibration methods. The calibration index updated in real time has better flexibility, and calibration early warning false alarms are reduced. The normal interval is defined by calculating the detection data in the normal detection time, the data is close to the actual detection environment, and the reliability is high. The data judgment and calculation process is simple, algorithm modeling does not need to be carried out by depending on a large amount of data, the universality is high, and the implementation cost is low. The calibration early warning judgment is carried out through all abnormal machines, the synchronization constraint is realized, and the stability of the calibration early warning is improved.
Description
Technical Field
The invention belongs to the technical field of detection equipment calibration methods, and particularly relates to a calibration early warning method of an online quality detection instrument.
Background
In the cigarette production process, the cigarette weight, circumference, suction resistance, ventilation and length data need to be detected by a quality detection instrument, but the detection error can be increased along with the increase of the detection times of the instrument, the detection result is easily influenced by the ambient temperature and humidity, and the accuracy of the instrument detection needs to be ensured by calibration and correction.
The existing calibration method is to calibrate the detection instrument at fixed time every day, the method cannot avoid time delay, if the equipment has errors, the error data cannot be judged, the accuracy of the detection equipment is influenced, the invalid calibration is carried out for a plurality of times when the equipment errors are within an allowable range, and the time cost of workers is wasted. In order to improve the accuracy of calibration early warning, detection equipment in other fields adopts an inflection point method and a machine learning algorithm to predict calibration time, the inflection point method identifies the peak and trough characteristics of a data curve and determines a calibration threshold value, and the early warning method can give out false alarms when cigarette defects occur and is difficult to meet the requirement of actual calibration. The machine learning algorithm needs a large amount of training, the error judgment of a quality detection instrument is difficult, the measurement results of cigarettes in the same batch in different time environments are different, the error reason is difficult to determine, a large amount of accurate and effective training sets cannot be provided, and the reliability of the algorithm in application is difficult to guarantee.
Disclosure of Invention
The calibration index is calculated through normal detection time measurement data, a normal interval is determined based on the calibration index, the calibration index is updated along with the input of detection data, whether the measurement state is normal or not is judged through whether the updated calibration index falls into the normal interval or not, if all the machines connected with the quality detector are abnormal, the current quality detector is judged to be calibrated, the calibration early warning accuracy is improved, the calibration mode flexibility and pertinence are improved, and the instrument calibration operation is more intelligent through real-time index monitoring and early warning.
In order to achieve the purpose, the invention adopts the following technical scheme: a calibration early warning method for an online quality detection instrument comprises the following steps:
and 6, updating the state queue until all machines connected with the current quality detection instrument finish one round of measurement data acquisition, if the current round of measurement state is suspected to be abnormal, determining that the current quality detection instrument needs to be calibrated, and recording the time of the measurement data as early warning calibration time.
Preferably, the step 3 includes the following steps:
step 31, calculating the mean and standard deviation:
mu＝mean(Xij)＝mean([x11,x12,…,xij])
s＝std(Xij)＝std([x11,x12,…,xij])
in the formula, Xij＝[x11,x12,…,xji]To determine the data, i represents the number of machines connected to the quality inspection apparatus, and j represents the number of products measured at a single time:
step 32, removing abnormal values to obtain new X'ij＝[x′11,x′12,…,x′ji′]：
In the formula, xijThe measured value of the jth product of the ith machine in the current measured object is represented; x is the number of-ijIndicating the current value as an abnormal valueAnd removing from the data set; x'ijRepresenting the result value after abnormal value test; ij' is the length of the data set after the abnormal value is eliminated;
step 33, calculating a calibration index Ri：
In the formula, CiThe technical standard value of cigarette brands produced for the current machine.
Preferably, the step 4 is based on the calibration index RiA 95% confidence interval is calculated, with the 95% confidence interval being the normal interval.
Preferably, in the step 2, a combined density curve is drawn through historical data, and the measurement data X in the time threshold is optimized through the combined density curveijSampling frequency and alternate duty ratio of each machine in the system.
The invention has the beneficial effects that:
dividing a period of time after the quality detection instrument is calibrated into normal detection time, calculating a calibration index according to data detected in the normal detection time, calculating a normal interval according to the calibration index, updating the calibration index according to the measurement data exceeding the normal detection time, judging whether the quality detection instrument is suspected to be abnormal according to the calibration index, and recording the time of the measurement data as early warning calibration time if all machines connected with the quality detection instrument are suspected to be abnormal. The flexibility of the calibration index updated in real time is superior to that of a fixed threshold value adopted by an inflection point method, and calibration early warning false alarms are reduced. The normal interval is defined by calculation of detection data in normal detection time, the data is close to an actual detection environment, and the reliability is high. The data judgment and calculation process is simple, algorithm modeling does not need to be carried out by depending on a large amount of data, the universality is high, and the implementation cost is low. The calibration early warning judgment is carried out through all abnormal machines, synchronization constraint is achieved, and the stability of calibration early warning is improved.
Drawings
FIG. 1 is a schematic flow chart of a calibration early warning method;
FIG. 2 is a schematic diagram of a layout structure of a quality inspection apparatus and a machine;
FIG. 3 is a diagram of the early warning effect of the quality detection instrument MTS-2004;
FIG. 4 is a diagram of the early warning effect of the quality detection instrument MTS-2005;
FIG. 5 is a diagram of the early warning effect of the quality detection instrument MTS-2008;
FIG. 6 is a diagram of the early warning effect of the quality detection instrument MTS-2010;
FIG. 7 shows a combined density curve of measurement times and calibration early warning times.
Detailed Description
In order to facilitate the understanding and implementation of the present invention for those skilled in the art, the technical solutions of the present invention will be further described with reference to the accompanying drawings and specific embodiments.
Taking the most frequently used quality detecting instrument in a rolling and packing workshop, namely a Retuo certain series of comprehensive test benches as an example, the measured data indexes comprise five indexes of weight, circumference, suction resistance, ventilation rate and length. The detection errors of the suction resistance and the ventilation rate can be increased along with the increase of the detection times of the instrument, and the instrument is easily influenced by the temperature and the humidity in the environment and is less interfered by human factors. The embodiment of the invention selects the suction resistance measurement data as the experimental data source. Selecting a quality detection instrument: MTS-2004, MTS-2005, MTS-2008, MTS-2010 and two product brands: a and B, the corresponding production scene layout structure is shown in FIG. 2, and data detection is carried out.
Preferably, the outliers are eliminated by the "3 σ" rule.
Step 31, calculating the mean and standard deviation:
mu＝mean(Xij)＝mean([x11,x12,…,xij]) (1)
s＝std(Xij)＝std([x11,x12,…,xij]) (2)
(1) in the formula (2), Xij＝[x11,x12,…,xji]To determine the data, i represents the number of machines connected to the quality inspection apparatus, and j represents the number of products measured at a single time:
step 32, removing abnormal values to obtain new X'ij＝[x′11,x′12,…,x′ji′]：
(3) In the formula, xijThe measured value of the jth product of the ith machine in the current measured object is represented; x is the number of-ijRepresenting the current value as an abnormal value and removing the current value from the data set; x'ijRepresenting the result value after abnormal value test; ij' is the length of the data set after the abnormal value is eliminated;
step 33, calculating a calibration index Ri: each measurement time on the current quality detector is TiThe corresponding calibration index is RiCalculating the normal detection time threshold T one by one1The calibration index of each measurement time.
(4) In the formula, CiThe technical standard value of cigarette brands produced for the current machine.
(5) in the formula
As a calibration index RiMean value, σ, as a calibration index RiStandard deviation, n is a calibration index RiAnd (4) total number.
When the quality detecting instrument measures time TiGreater than the normal detection time threshold T1Then, newly measure the measured data xj+1i+1Inputting and storing measurement data XijIn this case, Xij＝[x11,x12,…,xij,xi+1j+1]By updated XijAnd 3, repeating the step 3 to calculate the calibration index R 'corresponding to each newly measured measurement data'iCorresponding calibration index R'iSubstituting into the normal interval obtained in the step 4, and if the corresponding calibration index R'iIf the current measurement state falls into the normal interval, the current measurement state is a normal state, and if the corresponding calibration index R'iIf the current measurement state does not fall into the normal interval, the current measurement state is a suspected abnormal state.
And 6, updating the state queue until all machines connected with the current quality detection instrument finish one round of measurement data acquisition, if the current round of measurement state is suspected to be abnormal, determining that the current quality detection instrument needs to be calibrated, and recording the time of the measurement data as early warning calibration time.
(7) In the formula, NiRepresenting the state of data measured each time and calibrating an index R 'according to the state'iAfter the judgment, if the state is a normal state, marking as 1; if the abnormal state is suspected, it is marked as 0.
The state queue is K, K ═ N1，N2，…，Ni]The default length of the state queue is consistent with the number i of machines connected with the quality detection instrument, but in the actual measurement process, the assumed alternate machine measurement mechanism is broken, the length of the state queue K is increased until the state queue K contains at least one detection data of all machines, if the data of the state queue K is 0, the current measurement state is suspected to be abnormal, the current quality detection instrument is considered to have errors and needs to be calibrated, and the time for measuring the data is recorded as the early warning calibration time. Judging and inputting the detection data of the early warning calibration time, and if the detection data is in the measured data state NiStopping the early warning if the state is normal, and measuring the data state N if the state is normaliAnd if the abnormal state is suspected, continuously giving an early warning.
The specific embodiment is production data of a certain wrapping workshop, the quality detection instrument, the machine stations and the brand layout structure are shown in figure 2, a brand A cigarette is produced by a machine station of No. 1, No. 2, No. 7, No. 8 and No. 9, a brand B cigarette is produced by a machine station of No. 4, No. 5, No. 6, No. 10, No. 11 and No. 12, the MTS-2004 quality detection instrument is responsible for detecting cigarettes produced by the machine station of No. 1 and No. 2, the MTS-2005 quality detection instrument is responsible for detecting cigarettes produced by the machine station of No. 4, No. 5 and No. 6, the MTS-2008 quality detection instrument is responsible for detecting cigarettes produced by the machine station of No. 7, No. 8 and No. 9, the MTS2005 quality detection instrument is responsible for detecting cigarettes produced by the machine stations of No. 10, No. 11 and No. 12, the quality detection instrument is responsible for detecting cigarettes produced by the machine station of No. 10, No. 11 and No. 12
The early warning effect chart of the MTS-2004 quality detection instrument is shown in FIG. 3, after the first calibration is carried out by the standard component, the detection time of the previous 4 times of measured data is at the threshold value T of the normal detection time1In the method, a normal detection interval is defined through the previous 4 times of detection data, the 5 th time of measurement result is suspected to be abnormal, but at the moment, the state queue K is only recorded for 1 time of data, and the length of the state queue K is less than 2 machines connected with the MTS-2004 quality detection instrument, so that the calibration early warning is not triggered at the current point. And the 6 th measurement result is suspected to be abnormal, 1 state of each of the 1# machine and the 2# machine is recorded in the state queue K, the calibration early warning judgment standard is met, and the time of the measurement data is recorded as the early warning calibration time. The next 3 measurements belong to the 2# machine, and the status is suspected abnormal, and the corresponding status sequence is updated. And because the latest measured value of the 1# machine in the state sequence is in a suspected abnormal state, the calibration early warning is continuously sent out. And after actual calibration is carried out on the same day, a new early warning round is started. And the calibration early warning does not appear again in the rest measuring process.
The early warning effect diagram of the MTS-2005 quality inspection instrument is shown in FIG. 4, the machines test in turn and compare with the standard, and no calibration early warning occurs. In the detection process, the cigarette parameters produced by the 4# machine generally deviate from the existing technical standard values but do not exceed the abnormal value rejection interval, the data of the defective cigarette is reserved, and the detection data of the 4# machine is confirmed on site to be consistent with the actual production without errors.
An early warning effect diagram of the MTS-2008 quality inspection instrument is shown in FIG. 5, the 13 th measurement value has obvious mutation, but the value meets the existing brand technical range and is not removed as an abnormal value. In the actual detection process 15: after 00 minutes, sudden rainstorm cooling is carried out, the temperature and the humidity of the environment are changed, calibration early warning is continuously sent after 15:50, through on-site confirmation, the quality detection instrument is really influenced by the temperature and the humidity of the environment to generate errors, and the calibration early warning result is accurate and effective.
The early warning effect graph of the MTS-2010 quality inspection instrument is shown in FIG. 6. 19:06 triggers a calibration early warning. And then, although the corresponding measurement result and calibration index on the 11# machine station exceed the standard, the calibration indexes corresponding to the other two machine stations are normal, so that the early warning is not triggered again, the 11# machine station equipment is debugged after field confirmation and 19:00 time division, and the error of a quality detection instrument is caused by manual operation, so that the calibration early warning result is accurate and effective.
Preferably, in the step 2, a combined density curve is drawn through historical data, and the measurement data X in the time threshold is optimized through the combined density curveijSampling frequency and alternate duty ratio of each machine in the system.
As shown in fig. 7, the contour region in the graph is a joint density curve between the measurement times and the calibration early warning times in step 2; if the actual measurement times within 2 hours of calibration are less than 5 times, the range of the normal interval of the generated calibration index is narrowed, the sensitivity of calibration early warning is increased, and the times needing calibration are identified by the algorithm to be more. When the actual measurement times are more than or equal to 10, the calibration early warning times predicted by the method are generally smaller, the concentration degree of the density curve is better, and the algorithm calibration early warning effect is more stable.
The circle points in fig. 7 indicate the rotation ratio, and the darker the dot color indicates the higher the rotation ratio, the higher the rotation measurement satisfaction rate. And when the alternate occupation ratio is higher, the calibration early warning times are fewer, and the reasonableness is higher. From FIG. 7, it can be seen that the rotation ratio of the detection instrument MTS-2005 connecting three stations, MTS-2008 and MTS-2010 is significantly lower than that of the detection instrument MTS-2004 connecting two stations. If the rotation ratio is low, the distribution characteristics of the indexes of the machines extracted from the normal interval of the calibration indexes will be biased, and even the distribution characteristics of the indexes of the individual machines are missing, as shown in (1) - (2) subgraphs in fig. 7. In sub-diagrams (1) - (3), the measurement frequency of step 2 is less than 10, and the lower rotation ratio is the main reason for causing the additional protruded area on the left side in the way; the subgraph (4) belongs to a special condition, because the measurement does not occur within 2 hours after calibration, a normal interval of a calibration index cannot be defined, an algorithm automatically processes a subsequent measurement value into suspected abnormality and triggers calibration early warning.
Therefore, in conclusion, the measurement times and the rotation ratio in the step 2 are increased, and the calibration early warning accuracy and the calibration early warning stability are obviously improved. The two problems can be effectively avoided through a standardized operation flow, for example, each machine station is measured once every 25-30 minutes within 2 hours after actual calibration according to the specification of a quality detection instrument connected with 2 machine stations, and the measurement is carried out once every 30-40 minutes after the quality detection instrument connected with 3 machine stations is guaranteed. The time interval after 2 hours of calibration can be adjusted according to actual production requirements and expectations.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and the preferred embodiments of the present invention are described in the above embodiments and the description, and are not intended to limit the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (4)
1. A calibration early warning method for an online quality detection instrument is characterized by comprising the following steps:
step 1, calibrating a quality detection instrument for the first time by using a standard component to ensure that measurement data are consistent with the standard component, wherein the measurement time is T0；
Step 2, setting a normal detection time threshold T of the equipment1Time threshold T for normal detection of storage device1Internal measurement data Xij；
Step 3, based on the stored measurement data XijCalculating a calibration index Ri；
Step 4, based on the calibration index RiCalculating a corresponding normal interval;
step 5, the measuring time of the quality detecting instrument is larger than the normal detecting time threshold value T1And then, recalculating corresponding calibration index R 'according to the newly measured measurement data'iThen judging whether the current measurement state falls into a normal interval range, if so, determining that the current measurement state is a normal state, otherwise, determining that the current measurement state is a suspected abnormal state;
and 6, updating the state queue until all machines connected with the current quality detection instrument finish one round of measurement data acquisition, if the current round of measurement state is suspected to be abnormal, determining that the current quality detection instrument needs to be calibrated, and recording the time of the measurement data as early warning calibration time.
2. The calibration early warning method of the online quality detection instrument as claimed in claim 1, wherein the step 3 comprises the following steps: step 31, calculating the mean and standard deviation:
mu＝mean(Xij)＝mean([x11,x12,…,xij])
s＝std(Xij)＝std([x11,x12,…,xij])
in the formula, Xij＝[x11,x12,…,xji]To determine the data, i represents the number of machines connected to the quality inspection apparatus, and j represents the number of products measured at a single time:
step 32, removing abnormal values to obtain new X'ij＝[x′11,x′12,…,x′ji′]：
In the formula, xijThe measured value of the jth product of the ith machine in the current measured object is represented; x-ij represents that the current value is an abnormal value and is removed from the data set; x'ijRepresenting the result value after abnormal value test; ij' is the length of the data set after the abnormal value is eliminated;
step 33, calculating a calibration index Ri：
In the formula, CiThe technical standard value of cigarette brands produced for the current machine.
3. The calibration early warning method for on-line quality inspection instrument as claimed in claim 1, wherein said step 4 is based on a calibration index RiA 95% confidence interval is calculated, with the 95% confidence interval being the normal interval.
4. The calibration early warning method of the online quality inspection instrument as claimed in claim 1, wherein in the step 2, a combined density curve is drawn through historical data, and the measurement data X within the time threshold is optimized through the combined density curveijSampling frequency and alternate duty ratio of each machine in the system.
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