CN117131454A - X-ray thickness measurement abnormal data monitoring method - Google Patents
X-ray thickness measurement abnormal data monitoring method Download PDFInfo
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- CN117131454A CN117131454A CN202311369078.6A CN202311369078A CN117131454A CN 117131454 A CN117131454 A CN 117131454A CN 202311369078 A CN202311369078 A CN 202311369078A CN 117131454 A CN117131454 A CN 117131454A
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- 238000005259 measurement Methods 0.000 title claims abstract description 114
- 230000002159 abnormal effect Effects 0.000 title claims abstract description 54
- 238000000034 method Methods 0.000 title claims abstract description 51
- 238000012544 monitoring process Methods 0.000 title claims abstract description 24
- 238000012937 correction Methods 0.000 claims abstract description 29
- 230000005856 abnormality Effects 0.000 claims description 19
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- 238000004364 calculation method Methods 0.000 abstract description 4
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B15/00—Measuring arrangements characterised by the use of electromagnetic waves or particle radiation, e.g. by the use of microwaves, X-rays, gamma rays or electrons
- G01B15/02—Measuring arrangements characterised by the use of electromagnetic waves or particle radiation, e.g. by the use of microwaves, X-rays, gamma rays or electrons for measuring thickness
- G01B15/025—Measuring arrangements characterised by the use of electromagnetic waves or particle radiation, e.g. by the use of microwaves, X-rays, gamma rays or electrons for measuring thickness by measuring absorption
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
Abstract
The application relates to the technical field of measurement and calculation, and provides an X-ray thickness measurement abnormal data monitoring method, which comprises the following steps: acquiring X-ray thickness measurement parameters; calculating a vibration offset correction coefficient at each moment position according to the X-ray thickness measurement parameters, and acquiring object correction thickness measurement data to be measured at each moment position by using the vibration offset correction coefficient; calculating a thickness anomaly prominent value at each time position according to the object to be measured, correcting and thickness measurement data, calculating an anomaly proximity range at each time position according to the thickness anomaly prominent value, calculating a maximum proximity density distance at each time position according to the anomaly proximity range, and calculating local density at each time position according to the maximum proximity density distance; and calculating an outlier factor of the thickness measurement data at each moment position according to the local density, and monitoring the X-ray thickness measurement abnormal data according to the outlier factor. The application effectively reduces abnormal data in the X-ray thickness measurement process.
Description
Technical Field
The application relates to the technical field of measurement and calculation, in particular to an X-ray thickness measurement abnormal data monitoring method.
Background
The thickness of the X-ray thickness gauge is measured according to the attenuation degree of X-rays when the X-rays pass through an object, and the attenuation degree of the X-rays is related to the thickness of the object to be measured and the absorption coefficient of the material of the object to be measured on X. And the detector is used for receiving the X-rays penetrating through the object at the other end of the object, and converts the received electromagnetic breaking signal into a current signal. The current signal is amplified by the amplifying circuit and converted into a voltage signal, and the thickness of the measured object is calculated according to the attenuation condition of the X-rays judged by the voltage signal.
However, in the actual industrial measurement process, abnormal deviation exists in the thickness measurement data due to unstable running state of the measurement device, and the accurate measurement of the measured object is greatly affected.
Disclosure of Invention
The application provides an X-ray thickness measurement abnormal data monitoring method, which aims to solve the problem that the accuracy of the existing local outlier factor algorithm in the X-ray thickness measurement abnormal data monitoring process is poor, and the adopted technical scheme is as follows:
the application provides an X-ray thickness measurement abnormal data monitoring method, which comprises the following steps of:
acquiring X-ray thickness measurement parameters;
calculating a vibration offset correction coefficient at each moment position according to the X-ray thickness measurement parameters, and acquiring object correction thickness measurement data to be measured at each moment position by using the vibration offset correction coefficient;
calculating a thickness anomaly prominent value at each time position according to the object correction thickness measurement data at each time position, calculating an anomaly neighboring range at each time position according to the thickness anomaly prominent value at each time position, calculating a maximum neighboring density distance at each time position according to the anomaly neighboring range at each time position, and calculating a local density at each time position according to the maximum neighboring density distance at each time position;
and calculating an outlier factor of the thickness measurement data at each moment position according to the local density, and monitoring the X-ray thickness measurement abnormal data according to the outlier factor.
Preferably, the X-ray thickness measurement parameters include: thickness data and vibration offset distance of the object to be measured at each moment, and the distance from the vibration sensor to the farthest end of the object to be measured.
Preferably, the method for calculating the vibration offset correction coefficient at each moment position according to the X-ray thickness measurement parameter comprises the following steps:
the method comprises the steps of recording a difference value between the square of the furthest distance from a vibration sensor to an object to be measured and the square of the vibration offset distance of the object to be measured at each moment position as a first difference value, recording a ratio of the furthest distance from the vibration sensor to the object to be measured and the square of the first difference value as a vibration offset correction coefficient at each moment position, and recording a product of thickness data at each moment position and the vibration offset correction coefficient as corrected thickness data of the object to be measured at each moment position.
Preferably, the method for calculating the thickness abnormality prominent value at each time position according to the object to be measured correction thickness measurement data at each time position comprises the following steps:
and recording the difference between the corrected thickness measurement data at each time position and the corrected thickness measurement data mean value at different times in the preset length as a second difference value, recording the difference between the global variance and the local variance in the preset length of the corrected thickness measurement data at each time position as a third difference value, and recording the product of the second difference value, the third difference value and the preset length as a thickness abnormal prominent value at each time.
Preferably, the calculating method of the global variance and the local variance within the preset length of the corrected thickness measurement data comprises the following steps:
and marking the variances of all the corrected thickness measurement data in the preset length at each time position as global variances, and marking the variances of the data at the preset length at each time position without the current time position as local variances.
Preferably, the specific method for calculating the abnormal adjacent range at each time position according to the thickness abnormal prominent value at each time position is as follows:
in the above-mentioned formula(s),representing the preset length of the thickness measuring adjustment sequence, < >>Representation pair->Round upwards and fill up>Represents an exponential function based on natural constants, < ->Time +.>Maximum value of thickness abnormality prominent value sequence of lower preset length,/->Time +.>Minimum value of thickness abnormality highlighting value sequence of lower preset length, < ->Time +.>Mean value of thickness abnormality highlighting value sequence of lower preset length, +.>Indicating time->Abnormal proximity ranges at locations.
Preferably, the specific method for calculating the maximum adjacent density distance at each time position according to the abnormal adjacent range at each time position is as follows:
and calculating the difference value of the thickness anomaly prominent values at two adjacent time positions in the adjacent range at each time position, and recording the maximum difference value as the maximum adjacent density distance at each time position.
Preferably, the specific method for calculating the local density at each time position according to the maximum adjacent density distance at each time position is as follows:
in the above-mentioned formula(s),representing the preset length of the thickness measuring adjustment sequence, < >>Represents +.>Maximum proximity density distance at the individual time instant position, +.>Represents +.>Maximum proximity density distance of data at each time instant position, +.>Represents +.>Local density of data at the time instant locations.
Preferably, the method for calculating the outlier factor of the thickness measurement data at each time position according to the local density comprises the following steps:
and taking the local density at each time position as the input of a local outlier factor algorithm, and acquiring outlier factors of thickness measurement data at each different time position.
Preferably, the method for monitoring the X-ray thickness measurement abnormal data according to the outlier factor comprises the following steps:
traversing the X-ray thickness measurement data according to time sequence, and marking the thickness measurement data with the outlier factor larger than a preset value as abnormal data.
The beneficial effects of the application are as follows: according to the method, firstly, the vibration offset correction coefficient is calculated according to the vibration offset distance, the thickness measuring error caused by vibration offset in the X-ray thickness measuring process is weakened or even eliminated as much as possible, meanwhile, the object to be measured is obtained according to the vibration offset correction coefficient, the thickness measuring data is corrected, the thickness abnormal salient value is calculated and obtained, the thickness abnormal data condition in the X-ray thickness measuring process is represented, compared with the original thickness measuring data, the abnormal data characteristics caused by thickness measuring environment interference in the X-ray thickness measuring process are more obviously represented, further, the abnormal adjacent range is obtained according to the thickness abnormal salient value, the maximum adjacent density distance and the local density are obtained through calculation, the outlier factor in the X-ray thickness measuring process is obtained through optimizing the local outlier algorithm through the local density, the interference of the abnormal outlier data is eliminated, and the accuracy of X-ray thickness measuring is effectively ensured.
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In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the application, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a flow chart of a method for monitoring abnormal X-ray thickness measurement data according to an embodiment of the present application;
fig. 2 is a schematic diagram of the calculation of the X-ray thickness measurement vibration offset correction coefficient.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1, a flowchart of an X-ray thickness measurement anomaly data monitoring method according to an embodiment of the present application is shown, the method includes the following steps:
and S001, acquiring X-ray thickness measurement parameters.
It should be noted that, in the process of X-ray thickness measurement, the initial state of the X-ray thickness meter will have a great influence on the thickness measurement result, so that the X-ray thickness meter needs to be zeroed, so as to ensure that the thickness measurement data of the X-ray thickness meter is zero in the initial state without passing through any object to be measured. In the actual thickness measurement process, the object to be measured is caused to vibrate up and down unstably due to external force, so that the actual thickness measurement of the object to be measured can be greatly influenced. Thus, acquisition of thickness measurement related parameters is required.
Specifically, an object to be measured is obtained by an X-ray thickness gaugeThe thickness data at different moments constitute a thickness measuring data sequence, denoted +.>Wherein->Represents +.>X-ray thickness gauge data at each instant. Acquisition by vibration sensor->The vibration offset distances of the object to be measured at different moments form an amplitude sequence, which is recorded as。
Step S002, calculating vibration deviation correction coefficients at each time position according to the X-ray thickness measurement parameters, and obtaining object correction thickness measurement data to be measured at each time position by using the vibration deviation correction coefficients.
In the thickness measurement process of the X-ray thickness gauge, the object to be measured swings up and down to a certain extent due to the action of external force generated randomly, so that the accuracy of X-ray thickness measurement is greatly affected, and therefore, the measurement error caused by the deviation vibration condition of the object to be measured is required to be corrected.
Specifically, as shown in fig. 2, a plurality of dotted lines in the vertical direction represent X-rays in the thickness measuring process, a dotted line area represents a position before vibration deflection of the object to be measured, and a solid line rectangular area represents an actual position at the current time after vibration deflection of the object to be measured. In the drawing of the figure,and->The two angles are the same, and the current moment of the object to be measured is easy to know by the triangle similarity theorem and the Pythagorean theorem>The actual thickness>The transformation relation with the vibration amplitude is as follows:
in the above-mentioned formula(s),for time->Thickness of the object to be measured with vibration deflection down, +.>For the distance of the vibration sensor to the extreme end of the object to be measured, < > for>Time +.>Vibration offset distance of lower object to be measured, < >>Indicating the corrected thickness>The vibration deviation correction coefficient is shown.
When the vibration deviation of the object to be measured up and down occurs, the deviation exists between the measured thickness of the object to be measured and the actual thickness, so that when the vibration deviation distance value is larger, the fact that the larger deviation exists between the actual position of the object to be measured and the deviation position at the current moment is indicated, and the vibration deviation correction coefficient is obtained through the triangle geometric relationship to correct the thickness measurement distance of the object to be measured. Therefore, in order to weaken and even eliminate offset errors between actual thickness measurement data and actual thickness measurement data of an object to be measured caused by vibration offset as much as possible, correction is performed on the obtained thickness measurement data at each moment, and corrected thickness measurement data of the object to be measured are obtained.
Step S003, calculating a thickness abnormality prominence value at each time position according to the object correction thickness measurement data at each time position, calculating an abnormality proximity range at each time position according to the thickness abnormality prominence value at each time position, calculating a maximum proximity density distance at each time position according to the abnormality proximity range at each time position, and calculating a local density at each time position according to the maximum proximity density distance at each time position.
It should be noted that, in the actual thickness measurement process, because the running state of the X-ray thickness gauge is unstable, an abnormal state may occur due to strong X-rays, the stronger X-rays have higher energy and more easily pass through the object to be measured, and the weaker X-rays have lower energy, so that energy loss exists in penetrating the object to be measured, and the thickness measurement data is abnormal. Therefore, the object to be measured corrects the abnormal change condition of the thickness measurement data and further calculates and analyzes.
Specifically, in order to highlight the variation of abnormal data in the thickness measurement data sequence, firstly, the correction of the thickness measurement data value of the object to be measured needs to be adjusted, and the time length is assumedTo take the experience value +.>At the current moment +.>Is taken as the starting point and is taken forwards +.>And correcting the thickness measurement data of different objects to be measured to form a thickness measurement adjustment sequence.
In the above-mentioned formula(s),representing the current moment +.>Corrected thickness data at +.>Representing the current moment +.>The average value of all corrected thickness measurement data of the thickness measurement adjustment sequence as starting point, < >>Representing the current moment +.>Global variance of corrected thickness measurement data in thickness measurement adjustment sequence with position as starting point, +.>Representing the current moment +.>Removing time point +.>Local variance of all thickness data at the location, +.>Representing the preset length of the thickness measuring adjustment sequence, < >>Time +.>The thickness anomaly at the location highlights the value.
The time can be calculated by the formulaThe thickness abnormality at the position highlights the value, when +.>The larger the mean value difference between the corrected thickness measurement data and the thickness measurement adjustment sequence, and the moment +.>The larger the difference between the global variance calculated for all the different corrected thickness measurement data of the starting point and the local variance calculated for the thickness measurement adjustment sequence, the moment point calculated at this time is +.>The greater the thickness anomaly prominence value at the locationSay time point->The higher the likelihood of an outlier data point at a location.
If the time isThe greater the value of the thickness anomaly prominence calculated at the location, the current time is indicated +.>The higher the likelihood of anomaly in the measured thickness value at the location. After the thickness measurement data at different moments are processed by the steps, a thickness abnormality salient value sequence with preset length can be obtained, and if the position at a certain moment is abnormal thickness measurement data, larger difference exists between the thickness abnormality salient values of the data at the position at the moment adjacent to the different moments. In order to obtain the abnormal thickness measurement data points more accurately, a suitable proximity range needs to be determined first.
In the above-mentioned formula(s),representing the preset length of the thickness measuring adjustment sequence, < >>Representing an upward rounding function,/->Represents an exponential function based on natural constants, < ->Time +.>Maximum value of thickness abnormality prominent value sequence of lower preset length,/->Time +.>Minimum value of thickness abnormality highlighting value sequence of lower preset length, < ->Indicating the time of dayMean value of thickness abnormality highlighting value sequence of lower preset length, +.>Indicating time->Abnormal proximity range under.
The time can be calculated by the formulaThe magnitude of the next neighbor abnormality index at the time +.>The smaller the average value of the thickness anomaly salience index of the preset length is, but the larger the difference between the maximum value and the minimum value of the thickness anomaly salience value sequence is, which shows that the higher the possibility of anomaly data in the thickness measurement adjusting sequence of the preset length is, the search range of the thickness anomaly data is required to be reduced, and the obtained time is equal to>The smaller the proximity range below.
In the above-mentioned formula(s),representing the preset length of the thickness measuring adjustment sequence, < >>Represents +.>Maximum proximity density distance at the individual time instant position, +.>Represents +.>Maximum proximity density distance of data at each time instant position, +.>Represents +.>Local density of data at the time instant locations.
The first is calculated by the formulaThe reachable distances of the data density at the positions at each moment, when the reachable distances of the density calculated at different positions are smaller, all data points and data points are +.>The denser the distribution, the data points calculated at this time +.>The greater the local density value of (2); conversely, when all data points are within the preset length +.>The worse the distribution density, the calculated data point +.>The smaller the local density value of (2)。
And S004, calculating an outlier factor of the thickness measurement data at each moment position according to the local density, and monitoring the X-ray thickness measurement abnormal data according to the outlier factor.
The local density values can be calculated for the data points at different time positions, the local density values calculated at each different time position are taken as input to replace the local reachable density of the traditional local outlier factor algorithm, so that the outlier factors of the data at different time positions are calculated and recorded asThe specific process of obtaining outliers by a local outlier algorithm is known in the art and is not described herein in detail.
It should be noted that, when the outlier variation characteristics of the outlier data at different moments indicated by the magnitude of the outlier factor value obtained by optimizing the adjusted local outlier factor algorithm are larger than the calculated outlier factor valueAnd when the thickness measurement data at the current time position is considered to be abnormal data. Therefore, the thickness measurement data at all different time positions are traversed in time sequence, the thickness measurement data with the outlier factor value larger than 1 at the corresponding time position is marked as abnormal data, and the original thickness measurement data is marked as digital 0. Specifically, the data after the abnormal marking is traversed again, if the number 0 appears for a plurality of times, the abnormal occurrence of the X-ray thickness measuring equipment in the current X-ray thickness measuring process is indicated, and the X-ray thickness measuring equipment and the object to be measured need to be measured again after being adjusted.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application and are intended to be included within the scope of the application.
Claims (10)
1. An X-ray thickness measurement abnormal data monitoring method is characterized by comprising the following steps:
acquiring X-ray thickness measurement parameters;
calculating a vibration offset correction coefficient at each moment position according to the X-ray thickness measurement parameters, and acquiring object correction thickness measurement data to be measured at each moment position by using the vibration offset correction coefficient;
calculating a thickness anomaly prominent value at each time position according to the object correction thickness measurement data at each time position, calculating an anomaly neighboring range at each time position according to the thickness anomaly prominent value at each time position, calculating a maximum neighboring density distance at each time position according to the anomaly neighboring range at each time position, and calculating a local density at each time position according to the maximum neighboring density distance at each time position;
and calculating an outlier factor of the thickness measurement data at each moment position according to the local density, and monitoring the X-ray thickness measurement abnormal data according to the outlier factor.
2. The method of claim 1, wherein the X-ray thickness measurement anomaly data comprises: thickness data and vibration offset distance of the object to be measured at each moment, and the distance from the vibration sensor to the farthest end of the object to be measured.
3. The method for monitoring abnormal X-ray thickness measurement data according to claim 2, wherein the method for calculating the vibration offset correction coefficient at each time position according to the X-ray thickness measurement parameter is as follows:
the method comprises the steps of recording a difference value between the square of the furthest distance from a vibration sensor to an object to be measured and the square of the vibration offset distance of the object to be measured at each moment position as a first difference value, recording a ratio of the furthest distance from the vibration sensor to the object to be measured and the square of the first difference value as a vibration offset correction coefficient at each moment position, and recording a product of thickness data at each moment position and the vibration offset correction coefficient as corrected thickness data of the object to be measured at each moment position.
4. A method for monitoring abnormal thickness measurement data according to claim 3, wherein the method for calculating the abnormal thickness highlighting value at each time position according to the corrected thickness measurement data of the object to be measured at each time position comprises the steps of:
and recording the difference between the corrected thickness measurement data at each time position and the corrected thickness measurement data mean value at different times in the preset length as a second difference value, recording the difference between the global variance and the local variance in the preset length of the corrected thickness measurement data at each time position as a third difference value, and recording the product of the second difference value, the third difference value and the preset length as a thickness abnormal prominent value at each time.
5. The method for monitoring abnormal X-ray thickness measurement data according to claim 4, wherein the method for calculating global variance and local variance within a preset length of corrected thickness measurement data is as follows:
and marking the variances of all the corrected thickness measurement data in the preset length at each time position as global variances, and marking the variances of the data at the preset length at each time position without the current time position as local variances.
6. The method for monitoring abnormal X-ray thickness measurement data according to claim 4, wherein the specific method for calculating the abnormal proximity range at each time position according to the thickness abnormality prominent value at each time position is as follows:
in the above-mentioned formula(s),indicating thickness measurement adjustmentSequence preset length, & lt>Representation pair->Round upwards and fill up>Represents an exponential function based on natural constants, < ->Time +.>The maximum value of the thickness abnormality prominent numerical sequence of the lower preset length,time +.>Minimum value of thickness abnormality highlighting value sequence of lower preset length, < ->Time +.>Mean value of thickness abnormality highlighting value sequence of lower preset length, +.>Indicating time->Abnormal proximity ranges at locations.
7. The method for monitoring abnormal X-ray thickness measurement data according to claim 6, wherein the specific method for calculating the maximum adjacent density distance at each time position according to the abnormal adjacent range at each time position is as follows:
and calculating the difference value of the thickness anomaly prominent values at two adjacent time positions in the adjacent range at each time position, and recording the maximum difference value as the maximum adjacent density distance at each time position.
8. The method for monitoring abnormal X-ray thickness measurement data according to claim 7, wherein the specific method for calculating the local density at each time point according to the maximum adjacent density distance at each time point comprises the following steps:
in the above-mentioned formula(s),representing the preset length of the thickness measuring adjustment sequence, < >>Represents +.>Maximum proximity density distance at the individual time instant position, +.>Represents +.>Maximum proximity density distance of data at each time instant position, +.>Represents +.>Local density of data at the time instant locations.
9. The method for monitoring abnormal X-ray thickness measurement data according to claim 8, wherein the method for calculating outliers of thickness measurement data at each time position according to local density comprises:
and taking the local density at each time position as the input of a local outlier factor algorithm, and acquiring outlier factors of thickness measurement data at each different time position.
10. The method for monitoring abnormal X-ray thickness measurement data according to claim 1, wherein the method for monitoring abnormal X-ray thickness measurement data according to outlier factors comprises the following steps:
traversing the X-ray thickness measurement data according to time sequence, and marking the thickness measurement data with the outlier factor larger than a preset value as abnormal data.
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