CN107976432A - A kind of heat resisting steel aging level measurement method based on support vector machines - Google Patents
A kind of heat resisting steel aging level measurement method based on support vector machines Download PDFInfo
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Abstract
The invention discloses a kind of heat resisting steel aging level measurement method based on support vector machines, including step：(1) plasma spectrometry of the different heat-resisting steel samples of aging grade is obtained respectively in a manner of dot matrix measures using laser spectral analysis system；(2) characteristic strength variable composition spectrum data matrix is chosen and calculated from plasma light modal data；(3) spectrum data matrix is projected into high-order feature space by kernel function conversion；(4) algorithm of support vector machine structure calibration disaggregated model is utilized；(5) the luminescence of plasma spectrum of laser spectral analysis systematic survey heat-resisting steel sample to be measured is utilized, its spectrum data matrix is input in the calibration disaggregated model, obtains the aging grade of sample to be tested.Operation time of the present invention is short, can meet the real-time measurement of industry spot；There is outstanding robustness and adaptability to the metallographic inhomogeneities of steel, without arranging that complicated and excessive measurement point can accurately measure its aging grade to measurement object.
Description
Technical field
It is more particularly to a kind of to be lured based on laser the present invention relates to a kind of live rapid analysis method of heat resisting steel aging grade
Lead the rapid analysis method of the heat resisting steel aging grade of breakdown spectral combination supporting vector machine algorithm.
Background technology
Crucial large-scale heat power equipment in electric power, metallurgy, industries like chemical engineering and machinery is because need to be in the environment of high temperature and pressure
It is on active service, with the increase of active time, the metallic character of the heat-resisting steel part in heat power equipment can change, and gradually occur old
Change and damage, crack and internal injury so that the failure and accident probability of equipment greatly increase, and are directly related to whole production ring
The safety and benefit of section.So the aging grade of metal is an important indicator to equipment operation assessment.It is quick due to lacking
Effective detection technique, present factory are generally required by reducing the measure such as unit operation parameter, not yet due replacement heating part,
Reduced by sacrificing economy by accident rate.So need to develop it is a kind of can be quickly to the detection of heating surface aging grade
New technique.
According to People's Republic of China's power industry standard ---《DL/T884-2004, thermal power plant's metal inspection and evaluation
Technological guidance》, using the metallographic structure aging evaluation method and non-pearlite steel ageing tissues wherein in relation to non-pearlite steel
Feature evaluates the aging grade of heat resisting steel to consider the change of transgranular and grain boundary carbide, can be divided into 1 grade of (unaged), 2
Level (slight aging), 3 grades (mittlere alterungs), 4 grades (complete agings), 5 grades (serious agings).At present both at home and abroad to high-temperature metal portion
The status monitoring of part is mainly carried out in stop production to overhaul, using with the skill such as destructive pipe cutting detection technique and Non-Destructive Testing
Aging grade of the art to metal parts is assessed.Pipe cutting detection mainly checks chemical composition, metallographic structure and damage aging
Evaluation, room temperature and high temperature, short time mechanical property test, carbide phase constituent and Phase Structure Analysis, the experiment of dissimilar welding joint
Deng.The more detailed status information of the pipeline section can be obtained by pipe cutting detection, but pipeline section can so be damaged, and it is limited
The status information of pipeline section cannot represent the use state of other pipeline sections completely.Modern non-destructive testing technology is not damage measured object
On the basis of the structural behaviour or performance of body, the spy of sound, light, electricity, thermal and magnetic and ray etc. and matter interaction is utilized
Point, is detected important spare part and measures, and detects the defective of material and facility structure, judges its position, size, shape
Shape and species, and material property is assessed.The Non-Destructive Testing family members used at present mainly have ray detection, alternating current potential drop
Method detects.For high-temperature surface, when a defect is present, huge security risk there is.If can be in production period to height
Warm pressure-containing member is predicted its failure trend i.e. aging grade, then can pointedly emphasis supervision there are security risk portion
Part, to avoid there is serious accident.Therefore, the present invention proposes one kind and is based on support vector machines and laser-induced breakdown light
The live rapid analysis method of the T91 heat resisting steel aging grades of spectrum, to meet the needs of above-mentioned.
The content of the invention
Because traditional heat resisting steel high temperature failure analysis needs pipe cutting, and Non-Destructive Testing can only detect defective actual feelings
Condition.In order to overcome the above-mentioned deficiencies of the prior art, the present invention proposes one kind and is based on support vector machines and laser-induced breakdown light
The live rapid analysis method of the heat resisting steel aging grade of spectrum.By establishing spectrum of laser plasma and measurand metallographic group
The relation between change is knitted, builds aging grade detection model, the accurate of aging grade can be carried out to heat resisting steel to be measured and is judged.
The concrete technical scheme of the present invention is as follows：
A kind of heat resisting steel aging level measurement method based on support vector machines, including step：
(1) the heat-resisting steel sample of the 1-5 aging grades of selection standard first, is surveyed using laser spectral analysis system with dot matrix
The mode of amount obtains the plasma spectrometry of the different heat-resisting steel samples of aging grade respectively；
(2) characteristic strength variable composition spectrum data matrix is chosen and calculated from plasma light modal data；
(3) spectrum data matrix is projected into high-order feature space by kernel function conversion, obtains data distribution characteristics；
(4) according to the spectroscopic data distribution characteristics obtained, calibration disaggregated model is built using algorithm of support vector machine；
(5) the luminescence of plasma spectrum of laser spectral analysis systematic survey heat-resisting steel sample to be measured is utilized, by its spectrum
Data matrix is input in the calibration disaggregated model, obtains the aging grade of sample to be tested.
Further, the step (2) specifically includes：
(21) n group spectroscopic datas are obtained from the sample of each aging grade；
(22) the intensity of spectral line for including representative sample matrix element, representative sample alloy member are chosen from every group of spectroscopic data
The intensity of spectral line of element, intensity rate, the common m of the intensity rate of ion line and atom line of alloying element spectral line and matrix element spectral line
A spectral intensity variable, the spectrum data matrix of composition n × m dimensions, i.e.,：
Wherein, Xi=[X1i,2i,…,ni]tFor i-th of intensive variable；
Further, the sample matrices element is Fe.
Further, the sample alloy element includes Cr, Mo, Mn, V.
Further, the intensity rate of the alloying element spectral line and matrix element spectral line include Cr/Fe, Mn/Fe,
Mo/Fe、V/Fe。
Further, the intensity rate of the ion line and atom line includes FeII/FeI, CrII/CrI, MnII/
MnI。
Further, the step (3) projects after carrying out non-linear conversion to spectral variables matrix using kernel function
High-dimensional feature space, to realize that characteristic variable is extracted.Light is launched to the laser plasma of different samples using kernel function conversion
Spectrum carries out characteristic variable extraction, can effectively identify the data characteristics in the different heat-resisting steel samples of aging grade
Further, in the step (3), spectrum data matrix is projected into high-order feature sky by kernel function conversion
Between after, the method by building best decision hyperplane, i.e.,：
It is limited to yi(W·Φ(Xi)+b) >=1 ,=1,2 ..., to determine different aging grade heat resisting steel in high-order feature
Data distribution characteristics in space.
Further, the step (4) specifically includes：
(41) spectrum data matrix pre-process from calibration data；
(42) optimal model construction of SVM parameter is found using genetic algorithm；
(43) aging grade calibration disaggregated model is established based on support vector machines.
Further, the step (5) specifically includes：
(51) for needing detected heat-resisting steel surface, the pulse in portable laser spectrum quick analysis system is utilized
Laser continuously impacts steel surface, while by caused by the spectrometer collection in portable laser spectrum quick analysis system
Plasma spectrometry；
(52) plasma spectrometry input in the calibration disaggregated model from after scale pretreatment：
In formula, λiFor Lagrangian, yiTo model intensive variable collection X in the first stepiCorresponding aging grade label, K
(Xi, Z) and it is kernel function transformed representation；
(53) the aging grade of tested heat resisting steel is calculated according to f (Z), to realize aging level measurement.
Compared with prior art, the present invention has the following advantages：
1) present invention is carried using Laser Plasma Emission Spectrum progress characteristic variable of the kernel function conversion to different samples
Take, can effectively identify the data characteristics in the different heat-resisting steel samples of aging grade.
2) calibration disaggregated model is established to characteristic using support vector machines machine learning method, to the metallographics of steel not
Uniformity has outstanding robustness and adaptability, without arranging complicated and excessive measurement point to measurement object, you can accurate
Measure its aging grade.
3) data analysis process of the invention is automatically performed by computer program, and operation time is short, can meet industry spot
Real-time measurement.
4) without carrying out pipe cutting sampling to heat resisting pipe road to be measured, have close to it is lossless, that process is simple, accuracy rate is high etc. is excellent
Point.
Brief description of the drawings
Fig. 1 is the heat resisting steel aging level measurement method flow diagram based on support vector machines of the embodiment of the present invention.
Fig. 2 is the 200-600nm spectroscopic data exemplary plots for 1 sample of aging grade that the embodiment of the present invention detects.
Embodiment
The goal of the invention of the present invention is described in further detail with specific embodiment below in conjunction with the accompanying drawings, embodiment is not
It can repeat one by one herein, but therefore embodiments of the present invention are not defined in following embodiments.
Exemplified by using T91 heat resisting steel as detection object.
As shown in Figure 1, a kind of heat resisting steel aging level measurement method based on support vector machines, including step：
(1) heat-resisting steel samples of T91 of the 1-5 aging grades of selection standard first, using laser spectral analysis system with point
The mode of battle array measurement obtains the plasma spectrometry of the different heat-resisting steel samples of aging grade respectively；
(2) characteristic strength variable composition spectrum data matrix is chosen and calculated from plasma light modal data；
(3) conversion projects high-order feature space after spectrum data matrix being carried out non-linear conversion by kernel function, obtains
Take data distribution characteristics；
(4) according to the spectroscopic data distribution characteristics obtained, calibration disaggregated model is built using algorithm of support vector machine；
(5) the luminescence of plasma spectrum of laser spectral analysis systematic survey heat-resisting steel sample to be measured is utilized, by its spectrum
Data matrix is input in the calibration disaggregated model, obtains the aging grade of sample to be tested.
Specifically, as shown in Fig. 2, the step (2) specifically includes：
(21) 64 × 30 groups of spectroscopic datas as shown in Figure 1 are obtained from the sample of each aging grade；
(22) every group of data extract 63 characteristic spectral line intensity and 120 characteristic spectral line ratios, totally 183 variables.I.e.
1-5 agings rating sample obtains the spectrum data matrix of 183 × 64 × 30 dimensions altogether.Sample matrices element is Fe.Sample alloy member
Element includes Cr, Mo, Mn, V.The intensity rate of alloying element spectral line and matrix element spectral line include Cr/Fe, Mn/Fe, Mo/Fe,
V/Fe.The intensity rate of ion line and atom line includes FeII/FeI, CrII/CrI, MnII/MnI.
Specifically, in the step (3), spectrum data matrix is projected into high-order feature sky by kernel function conversion
Between after, the method by building best decision hyperplane, i.e.,：
It is limited to yi(W·Φ(Xi)+b) >=1 ,=1,2 ..., to determine different aging grade heat resisting steel in high-order feature
Data distribution characteristics in space.
Specifically, the step (5) specifically includes：
(51) for needing detected heat-resisting steel surface, the pulse in portable laser spectrum quick analysis system is utilized
Laser continuously impacts pipe surface, obtains the spectroscopic data of object to be analyzed, while quickly analyze by portable laser spectrum
Plasma spectrometry caused by spectrometer collection in system；
(52) plasma spectrometry input in the calibration disaggregated model from after scale pretreatment：
In formula, λiFor Lagrangian, yiTo model intensive variable collection X in the first stepiCorresponding aging grade label, K
(Xi, Z) and it is kernel function transformed representation；
(53) the aging grade of tested heat resisting steel is calculated according to f (Z), to realize aging level measurement.
The above embodiment of the present invention is only intended to clearly illustrate example of the present invention, and is not to the present invention
Embodiment restriction.For those of ordinary skill in the field, can also make on the basis of the above description
Other various forms of changes or variation.There is no necessity and possibility to exhaust all the enbodiments.It is all the present invention
All any modification, equivalent and improvement made within spirit and principle etc., should be included in the protection of the claims in the present invention
Within the scope of.
Claims (10)
- A kind of 1. heat resisting steel aging level measurement method based on support vector machines, it is characterised in that including step：(1) the heat-resisting steel sample of the 1-5 aging grades of selection standard first, is measured using laser spectral analysis system with dot matrix Mode obtains the plasma spectrometry of the different heat-resisting steel samples of aging grade respectively；(2) characteristic strength variable composition spectrum data matrix is chosen and calculated from plasma light modal data；(3) spectrum data matrix is projected into high-order feature space by kernel function conversion, obtains data distribution characteristics；(4) according to the spectroscopic data distribution characteristics obtained, calibration disaggregated model is built using algorithm of support vector machine；(5) the luminescence of plasma spectrum of laser spectral analysis systematic survey heat-resisting steel sample to be measured is utilized, by its spectroscopic data Input matrix obtains the aging grade of sample to be tested into the calibration disaggregated model.
- 2. the heat resisting steel aging level measurement method according to claim 1 based on support vector machines, it is characterised in that：Institute The step of stating (2) specifically includes：(21) n group spectroscopic datas are obtained from the sample of each aging grade；(22) being chosen from every group of spectroscopic data includes the intensity of spectral line of representative sample matrix element, representative sample alloying element The intensity rate, the common m light of the intensity rate of ion line and atom line of the intensity of spectral line, alloying element spectral line and matrix element spectral line Spectral intensity variable, the spectrum data matrix of composition n × m dimensions, i.e.,：Wherein, Xi=[X1i, X2i..., Xni]tFor i-th of intensive variable.
- 3. the heat resisting steel aging level measurement method according to claim 2 based on support vector machines, it is characterised in that：Institute The sample matrices element stated is Fe.
- 4. the heat resisting steel aging level measurement method according to claim 2 based on support vector machines, it is characterised in that：Institute The sample alloy element stated includes Cr, Mo, Mn, V.
- 5. the heat resisting steel aging level measurement method according to claim 2 based on support vector machines, it is characterised in that：Institute The intensity rate of the alloying element spectral line stated and matrix element spectral line includes Cr/Fe, Mn/Fe, Mo/Fe, V/Fe.
- 6. the heat resisting steel aging level measurement method according to claim 2 based on support vector machines, it is characterised in that：Institute The intensity rate of the ion line stated and atom line includes FeII/FeI, CrII/CrI, MnII/MnI.
- 7. the heat resisting steel aging level measurement method according to claim 1 based on support vector machines, it is characterised in that：Institute The step of stating (3) projects high-dimensional feature space after carrying out non-linear conversion to spectral variables matrix using kernel function, to realize Characteristic variable is extracted.
- 8. the heat resisting steel aging level measurement method according to claim 1 based on support vector machines, it is characterised in that：Institute In the step of stating (3), after spectrum data matrix is projected high-order feature space by kernel function conversion, by building optimal determine The method of plan hyperplane, i.e.,：It is limited to yi(W·Φ(Xi)+b) >=1, i=1,2 ..., n, to determine that different aging grade heat resisting steel are empty in high-order feature Between in data distribution characteristics.
- 9. the heat resisting steel aging level measurement method according to claim 1 based on support vector machines, it is characterised in that：Institute The step of stating (4) specifically includes：(41) spectrum data matrix pre-process from calibration data；(42) optimal model construction of SVM parameter is found using genetic algorithm；(43) aging grade calibration disaggregated model is established based on support vector machines.
- 10. the heat resisting steel aging level measurement method according to claim 1 based on support vector machines, it is characterised in that： The step (5) specifically includes：(51) for needing detected heat-resisting steel surface, the pulse laser in portable laser spectrum quick analysis system is utilized Continuous impact steel surface, at the same by caused by the spectrometer collection in portable laser spectrum quick analysis system etc. from Daughter spectrum；(52) plasma spectrometry input in the calibration disaggregated model from after scale pretreatment：In formula, λiFor Lagrangian, yiTo model intensive variable collection X in the first stepiCorresponding aging grade label, K (Xi, Z) it is kernel function transformed representation；(53) the aging grade of tested heat resisting steel is calculated according to f (Z), to realize aging level measurement.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109086547A (en) * | 2018-08-23 | 2018-12-25 | 华南理工大学 | A kind of ageing of metal level measurement method |
| CN109521041A (en) * | 2018-11-30 | 2019-03-26 | 华南理工大学 | A kind of XLPE material heat ageing dynamic process multiphase associated detecting method |
| CN109741298A (en) * | 2018-12-06 | 2019-05-10 | 东北大学 | Semi-continuous casting alusil alloy microstructure appraisal procedure |
| CN112001446A (en) * | 2020-08-25 | 2020-11-27 | 中国特种设备检测研究院 | Method and device for determining aging grade of high-chromium martensite heat-resistant steel structure |
| CN112816553A (en) * | 2021-01-22 | 2021-05-18 | 国电锅炉压力容器检验有限公司 | Heat-resistant steel aging grade evaluation method based on support vector machine |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104200217A (en) * | 2014-08-07 | 2014-12-10 | 哈尔滨工程大学 | Hyperspectrum classification method based on composite kernel function |
| CN106092916A (en) * | 2016-06-05 | 2016-11-09 | 丁旭秋 | Coal mine gas detection method |
| CN106404753A (en) * | 2016-10-18 | 2017-02-15 | 中国大唐集团科学技术研究院有限公司西北分公司 | Classification and verification method of iron ore |
| CN106706599A (en) * | 2016-11-22 | 2017-05-24 | 广东电网有限责任公司电力科学研究院 | Ageing grade analysis method of heated surface of boiler |
| CN106841085A (en) * | 2016-06-05 | 2017-06-13 | 乌鲁木齐职业大学 | Gas measuring method based on KPCA |
| CN106885978A (en) * | 2017-04-20 | 2017-06-23 | 重庆大学 | A kind of paper oil insulation Diagnosis of Aging based on insulating oil Raman spectrum wavelet-packet energy entropy |
-
2017
- 2017-10-16 CN CN201710962614.1A patent/CN107976432B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104200217A (en) * | 2014-08-07 | 2014-12-10 | 哈尔滨工程大学 | Hyperspectrum classification method based on composite kernel function |
| CN106092916A (en) * | 2016-06-05 | 2016-11-09 | 丁旭秋 | Coal mine gas detection method |
| CN106841085A (en) * | 2016-06-05 | 2017-06-13 | 乌鲁木齐职业大学 | Gas measuring method based on KPCA |
| CN106404753A (en) * | 2016-10-18 | 2017-02-15 | 中国大唐集团科学技术研究院有限公司西北分公司 | Classification and verification method of iron ore |
| CN106706599A (en) * | 2016-11-22 | 2017-05-24 | 广东电网有限责任公司电力科学研究院 | Ageing grade analysis method of heated surface of boiler |
| CN106885978A (en) * | 2017-04-20 | 2017-06-23 | 重庆大学 | A kind of paper oil insulation Diagnosis of Aging based on insulating oil Raman spectrum wavelet-packet energy entropy |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109086547A (en) * | 2018-08-23 | 2018-12-25 | 华南理工大学 | A kind of ageing of metal level measurement method |
| CN109521041A (en) * | 2018-11-30 | 2019-03-26 | 华南理工大学 | A kind of XLPE material heat ageing dynamic process multiphase associated detecting method |
| CN109521041B (en) * | 2018-11-30 | 2022-05-17 | 华南理工大学 | XLPE material thermal aging dynamic process multiphase combined detection method |
| CN109741298A (en) * | 2018-12-06 | 2019-05-10 | 东北大学 | Semi-continuous casting alusil alloy microstructure appraisal procedure |
| CN112001446A (en) * | 2020-08-25 | 2020-11-27 | 中国特种设备检测研究院 | Method and device for determining aging grade of high-chromium martensite heat-resistant steel structure |
| CN112816553A (en) * | 2021-01-22 | 2021-05-18 | 国电锅炉压力容器检验有限公司 | Heat-resistant steel aging grade evaluation method based on support vector machine |
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