CN115684737B - Algorithm for calculating waveform glitch - Google Patents
Algorithm for calculating waveform glitch Download PDFInfo
- Publication number
- CN115684737B CN115684737B CN202211326856.9A CN202211326856A CN115684737B CN 115684737 B CN115684737 B CN 115684737B CN 202211326856 A CN202211326856 A CN 202211326856A CN 115684737 B CN115684737 B CN 115684737B
- Authority
- CN
- China
- Prior art keywords
- waveform
- derivative
- test waveform
- test
- burr
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 9
- 238000012360 testing method Methods 0.000 claims abstract description 65
- 230000002950 deficient Effects 0.000 claims abstract description 11
- 238000009795 derivation Methods 0.000 claims abstract description 11
- 238000004364 calculation method Methods 0.000 claims abstract description 7
- 238000012797 qualification Methods 0.000 abstract description 2
- 238000013139 quantization Methods 0.000 abstract 1
- 230000010355 oscillation Effects 0.000 description 3
- 238000000034 method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000009413 insulation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The invention relates to an algorithm for calculating waveform glitches, comprising obtaining a test waveform; performing first-order derivation on the test waveform data; taking absolute value of the first derivative of the test waveform, and then adding the absolute value of the first derivative of the test waveform in an accumulated way to obtain the waveform area of the first derivative of the test waveform; subtracting the area of the first derivative waveform of the test waveform from the area of the first derivative waveform of the standard waveform to obtain a burr total amount; comparing the total burr amount of the test waveform with a preset standard value, and judging whether the test waveform is defective; performing second-order derivation on the test waveform data; taking absolute values of the second derivative of the test waveform, wherein the maximum value is the maximum burr; and comparing the maximum burr of the test waveform with a preset standard value, and judging whether the test waveform is defective. According to the invention, the test waveform data is processed in a mathematical calculation mode, the total amount of burrs and the maximum burrs are subjected to digital quantization, and the burrs are comprehensively judged, so that the qualification rate of products is accurately judged.
Description
Technical Field
The invention relates to the technical field of pulse testing, in particular to an algorithm for calculating waveform burrs.
Background
In the pulse test process of the pulse coil and the pulse battery core, the pulse oscillation waveform is mapped first and then compared with the standard waveform to judge the quality of the product. When the partial insulation of the product is poor, a partial discharge phenomenon is caused, and thus the poor condition is reflected on the waveform in the form of burrs.
However, instead of determining that the burr is defective as soon as it occurs, it is necessary to determine defective by comprehensively determining the number of burrs and the size of the burrs.
Currently, there is no method for calculating the total amount of burrs and the maximum burrs in the prior art.
Disclosure of Invention
The invention aims to solve the technical problems that: an algorithm for calculating waveform glitches is provided, which obtains the total amount of glitches and the maximum glitches by digitally quantizing the glitches in the waveform.
The technical scheme adopted for solving the technical problems is as follows: an algorithm for calculating waveform glitches, comprising the steps of,
s1, acquiring a test waveform;
s2, performing first-order derivation on the test waveform data to obtain a first derivative of the test waveform;
s3, taking absolute values of the first derivative of the test waveform, and then adding the absolute values in an accumulated manner to obtain the waveform area of the first derivative of the test waveform;
s4, subtracting the area of the first derivative waveform of the test waveform from the area of the first derivative waveform of the standard waveform to obtain a burr total amount;
s5, comparing the total burr amount of the test waveform with a preset standard value, and judging whether the test waveform is a defective product or not;
s6, performing second-order derivative on the test waveform data to obtain a second-order derivative of the test waveform;
s7, taking an absolute value of a second derivative of the test waveform, wherein the maximum value is the maximum burr;
s8, comparing the maximum burr of the test waveform with a preset standard value, and judging whether the test waveform is defective.
Further, in step S2 of the present invention, the first-order derivative formula of the test waveform is:
where V is waveform data and dt is time interval.
Further, in step S3 of the present invention, the calculation formula of the area of the first derivative waveform of the test waveform is:
further, in step S6 of the present invention, the second derivative formula of the test waveform is:
wherein,as the first derivative, dt is the time interval.
Further, in step S7 of the present invention, the maximum burr of the test waveform=
The invention has the beneficial effects that the defects in the background technology are overcome, the test waveform data is processed in a mathematical calculation mode, the total amount of burrs and the maximum burrs are digitally quantized, the burrs are comprehensively judged, and the qualification rate of products is accurately judged.
Drawings
FIG. 1 is an oscillating waveform of a standard product;
fig. 2 is an oscillating waveform (containing burrs) of a poor product;
FIG. 3 is a first derivative waveform of a standard waveform;
FIG. 4 is a first derivative waveform with glitches;
FIG. 5 is a second derivative waveform of a standard waveform;
fig. 6 is a second derivative waveform with glitches.
Detailed Description
The invention will now be described in further detail with reference to the drawings and a preferred embodiment. The drawings are simplified schematic representations which merely illustrate the basic structure of the invention and therefore show only the structures which are relevant to the invention.
An algorithm for calculating waveform glitches, as shown in fig. 1-6, includes the steps of,
s1, acquiring a test waveform;
s2, performing first-order derivation on the test waveform data to obtain a first derivative of the test waveform;
s3, taking absolute values of the first derivative of the test waveform, and then adding the absolute values in an accumulated manner to obtain the waveform area of the first derivative of the test waveform;
s4, subtracting the area of the first derivative waveform of the test waveform from the area of the first derivative waveform of the standard waveform to obtain a burr total amount;
s5, comparing the total burr amount of the test waveform with a preset standard value, and judging whether the test waveform is a defective product or not;
s6, performing second-order derivative on the test waveform data to obtain a second-order derivative of the test waveform;
s7, taking an absolute value of a second derivative of the test waveform, wherein the maximum value is the maximum burr;
s8, comparing the maximum burr of the test waveform with a preset standard value, and judging whether the test waveform is defective.
The following is a detailed description.
Fig. 1 shows an oscillation waveform of a standard product, and fig. 2 shows an oscillation waveform of a defective product, which contains burrs. The burr can be understood as a sudden change on the waveform, in order to clearly observe the sudden change, the waveform is subjected to first order derivation to obtain a first derivative waveform, as shown in fig. 3, which is a first derivative waveform of a standard waveform, and fig. 4, which is a first derivative waveform with the burr, and it can be clearly seen that the first derivative value at the burr is very large.
The test waveform data is subjected to first-order derivation, and the calculation formula is as follows
Adding the absolute values of the first derivatives, obtaining a first derivative waveform area S as a result, and calculating the waveform area S according to the formula:
subtracting the area of the first derivative waveform from the area of the first derivative waveform of the standard waveform to obtain a total burr amount, wherein the calculation formula is as follows: total amount of burrs = S2-S1.
If the total amount of burrs is larger than a preset standard value, burrs on the measured waveform are more than the standard waveform, and the product is judged to be bad.
The sharpness of the burr can be understood as the change of the curvature of the waveform, and the larger the change of the curvature is, the sharper the burr is indicated. In order to clearly observe the curvature change of the waveform curve, the test waveform is subjected to second derivative and takes an absolute value to obtain a second derivative waveform, such as the second derivative waveform of the standard waveform in fig. 5, and the second derivative waveform with burrs in fig. 6. It can be seen from the figure that the second derivative value at the burr of figure 6 is larger.
Performing second-order derivation on the test waveform data, namely performing first-order derivation on the first derivative, wherein a calculation formula is as follows:
taking absolute value of the second derivative, and taking the maximum value as the maximum burr.
If the maximum burr is greater than the preset standard value, the product is judged to be bad.
Let n data be the complete waveform data V.
D due to fixed sampling rate t Fixed value, d is assumed to be 1 in time interval t ＝1；
1. First order derivation of waveform data
V′ 1 ＝V 2 -V 1
V′ 2 ＝V 3 -V 2
V′ 3 ＝V 4 -V 3
……
V′ n-1 ＝V n -V n-1
N-1 first order derivative data are used;
S2＝|V 2 -V 1 |+|V 3 -V 2 |+|V 4 -V 3 |+…+|V n -V n-1 |
knowing the area S1 of the standard waveform,
total amount of burrs = S2-S1.
2. Second order derivation of waveforms
V′ 1 ′＝V 2 ′-V 1 ′
V′ 2 ′＝V 3 ′-V 2 ′
V′ 3 ′＝V 4 ′-V 3 ′
……
V′ n-2 ′＝V n-1 ′-V n-2 ′
N-2 second order derivative data,
maximum burr
The foregoing description is merely illustrative of specific embodiments of the invention, and the invention is not limited to the details shown, since modifications and variations of the foregoing embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention.
Claims (2)
1. An algorithm for calculating waveform glitch, characterized by: comprises the steps of,
s1, acquiring a test waveform;
s2, performing first-order derivation on the test waveform data to obtain a first derivative of the test waveform;
the first-order derivative formula of the test waveform is as follows:
wherein V is waveform data, and dt is time interval;
s3, taking absolute values of the first derivative of the test waveform, and then adding the absolute values in an accumulated manner to obtain the waveform area of the first derivative of the test waveform;
the area calculation formula of the first derivative waveform of the test waveform is as follows:
s4, subtracting the area of the first derivative waveform of the test waveform from the area of the first derivative waveform of the standard waveform to obtain a burr total amount;
s5, comparing the total burr amount of the test waveform with a preset standard value, and judging whether the test waveform is a defective product or not;
s6, performing second-order derivative on the test waveform data to obtain a second-order derivative of the test waveform;
the test waveform second order derivative formula is:
wherein,as the first derivative, dt is the time interval;
s7, taking an absolute value of a second derivative of the test waveform, wherein the maximum value is the maximum burr;
s8, comparing the maximum burr of the test waveform with a preset standard value, and judging whether the test waveform is defective.
2. An algorithm for calculating waveform glitches of claim 1 in which: in the step S7 described above, a step of,
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211326856.9A CN115684737B (en) | 2022-10-26 | 2022-10-26 | Algorithm for calculating waveform glitch |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211326856.9A CN115684737B (en) | 2022-10-26 | 2022-10-26 | Algorithm for calculating waveform glitch |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115684737A CN115684737A (en) | 2023-02-03 |
| CN115684737B true CN115684737B (en) | 2023-11-17 |
Family
ID=85098577
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211326856.9A Active CN115684737B (en) | 2022-10-26 | 2022-10-26 | Algorithm for calculating waveform glitch |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115684737B (en) |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5639962A (en) * | 1996-03-06 | 1997-06-17 | The Goodyear Tire & Rubber Company | Enhanced tire uniformity machine data utilizing first and second derivative calculations |
| CN102193900A (en) * | 2011-07-01 | 2011-09-21 | 国电南京自动化股份有限公司 | Peak recognition algorithm based on first-order derivative characteristic |
| JP2015049136A (en) * | 2013-09-02 | 2015-03-16 | 株式会社島津製作所 | Peak extraction method and program |
| CN105067650A (en) * | 2015-08-03 | 2015-11-18 | 东南大学 | Method for calculating characteristic peak of derivative detection spectrum through using wavelet |
| CN106503681A (en) * | 2016-10-28 | 2017-03-15 | 东华大学 | A kind of digital picture stage division of wool scale layer |
| CN106687033A (en) * | 2014-09-04 | 2017-05-17 | 日本电信电话株式会社 | Heartbeat detection method and heartbeat detection device |
| CN106908655A (en) * | 2017-03-06 | 2017-06-30 | 广东顺德工业设计研究院(广东顺德创新设计研究院) | Photosignal peak-value detection method and system |
| RU2639735C1 (en) * | 2016-10-03 | 2017-12-22 | федеральное государственное бюджетное образовательное учреждение высшего образования "Пермский национальный исследовательский политехнический университет" | Method of estimation of dilatometric studies of phase transformations in iron alloys |
| CN110084212A (en) * | 2019-04-30 | 2019-08-02 | 河南师范大学 | A kind of spectral signature peak recognition positioning method based on improvement sine and cosine algorithm |
| CN110542441A (en) * | 2019-10-10 | 2019-12-06 | 华北电力大学（保定） | Signal demodulation method of optical fiber Bragg grating sensing system |
| CN112016045A (en) * | 2020-07-21 | 2020-12-01 | 中国科学院西安光学精密机械研究所 | Data processing method of nanosecond pulse power meter |
| CN112730545A (en) * | 2020-12-22 | 2021-04-30 | 华中科技大学 | Online prediction method and system for electrode coking amount in biomass pyrolysis oil electrolysis process |
| CN112836166A (en) * | 2021-01-15 | 2021-05-25 | 北京科技大学 | Response peak analysis-based first-order differential algorithm for monitoring data of equal sampling experiment |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160081631A1 (en) * | 2014-09-23 | 2016-03-24 | Apex Medical Corp. | Respiratory waveform recognition method and system |
| US10928367B2 (en) * | 2016-02-03 | 2021-02-23 | Shimadzu Corporation | Peak extraction method and program |
-
2022
- 2022-10-26 CN CN202211326856.9A patent/CN115684737B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5639962A (en) * | 1996-03-06 | 1997-06-17 | The Goodyear Tire & Rubber Company | Enhanced tire uniformity machine data utilizing first and second derivative calculations |
| CN102193900A (en) * | 2011-07-01 | 2011-09-21 | 国电南京自动化股份有限公司 | Peak recognition algorithm based on first-order derivative characteristic |
| JP2015049136A (en) * | 2013-09-02 | 2015-03-16 | 株式会社島津製作所 | Peak extraction method and program |
| CN106687033A (en) * | 2014-09-04 | 2017-05-17 | 日本电信电话株式会社 | Heartbeat detection method and heartbeat detection device |
| CN105067650A (en) * | 2015-08-03 | 2015-11-18 | 东南大学 | Method for calculating characteristic peak of derivative detection spectrum through using wavelet |
| RU2639735C1 (en) * | 2016-10-03 | 2017-12-22 | федеральное государственное бюджетное образовательное учреждение высшего образования "Пермский национальный исследовательский политехнический университет" | Method of estimation of dilatometric studies of phase transformations in iron alloys |
| CN106503681A (en) * | 2016-10-28 | 2017-03-15 | 东华大学 | A kind of digital picture stage division of wool scale layer |
| CN106908655A (en) * | 2017-03-06 | 2017-06-30 | 广东顺德工业设计研究院(广东顺德创新设计研究院) | Photosignal peak-value detection method and system |
| CN110084212A (en) * | 2019-04-30 | 2019-08-02 | 河南师范大学 | A kind of spectral signature peak recognition positioning method based on improvement sine and cosine algorithm |
| CN110542441A (en) * | 2019-10-10 | 2019-12-06 | 华北电力大学（保定） | Signal demodulation method of optical fiber Bragg grating sensing system |
| CN112016045A (en) * | 2020-07-21 | 2020-12-01 | 中国科学院西安光学精密机械研究所 | Data processing method of nanosecond pulse power meter |
| CN112730545A (en) * | 2020-12-22 | 2021-04-30 | 华中科技大学 | Online prediction method and system for electrode coking amount in biomass pyrolysis oil electrolysis process |
| CN112836166A (en) * | 2021-01-15 | 2021-05-25 | 北京科技大学 | Response peak analysis-based first-order differential algorithm for monitoring data of equal sampling experiment |
Non-Patent Citations (2)
| Title |
|---|
| 基于二阶导数脉搏波极值点识别的算法;梁建武 等;电脑知识与技术;第9卷(第16期);第3850-3855页 * |
| 对称零面积变换法找峰;庞巨手 等;原子能科学技术(第3期);第271-279页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115684737A (en) | 2023-02-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| TW202212843A (en) | Detection method, system, electronic equipment, and storage medium of product test data | |
| CN110816588B (en) | Locomotive wheel out-of-roundness detection method, device, equipment and system | |
| CN107086039B (en) | Audio signal processing method and device | |
| KR20180136366A (en) | System and method for adaptive testing of semiconductor product | |
| CN115684737B (en) | Algorithm for calculating waveform glitch | |
| CN114839269A (en) | Nondestructive testing method and device for internal defect stress of GIS solid insulating part | |
| JP2009156650A (en) | Strength estimation apparatus | |
| CN115047065A (en) | Method, device and equipment for correcting impact of knocking force in beating method | |
| CN111614408B (en) | RF test method of Zigbee module | |
| CN117200242A (en) | Monitoring data processing method and system for intelligent voltage regulating cabinet | |
| CN111766298A (en) | Novel anchor rod nondestructive testing method | |
| CN116400286A (en) | Electronic voltage sensor | |
| CN115828144A (en) | Signal sparse representation and fusion detection method, storage medium and electronic device | |
| JP2012127774A (en) | Corrosion occurrence detection method, corrosion occurrence detection device and corrosion occurrence detection system | |
| CN114841573A (en) | System and method for determining grouping inspection scheme of exponential life type product | |
| CN107908133A (en) | A kind of frequency adaptively gathers distribution method | |
| CN114157023A (en) | Distribution transformer early warning information acquisition method | |
| CN107659466B (en) | System and method for quantifying and calculating throughput capacity service level of cloud system | |
| JP3684058B2 (en) | Glitch noise inspection method and apparatus | |
| CN110161330B (en) | Method and device for detecting vibration sound of transformer running state based on gray scale theory | |
| CN113759223B (en) | Oscillating wave partial discharge data conversion method and related device | |
| CN110231222A (en) | A method of the exfactory inspection of disc insulator mechanical performance is carried out using indentation method | |
| CN114167246B (en) | Measuring method and evaluation of voltage rising rate of verification parameter of lightning protection element tester | |
| JP3085853B2 (en) | Transformer insulation condition monitoring device | |
| CN114023540B (en) | Method, device, equipment and storage medium for monitoring transformer lifting seat and sleeve |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |