CN106528842A - Method for screening import data segments of magnetic flux leakage detection data - Google Patents
Method for screening import data segments of magnetic flux leakage detection data Download PDFInfo
- Publication number
- CN106528842A CN106528842A CN201611042000.3A CN201611042000A CN106528842A CN 106528842 A CN106528842 A CN 106528842A CN 201611042000 A CN201611042000 A CN 201611042000A CN 106528842 A CN106528842 A CN 106528842A
- Authority
- CN
- China
- Prior art keywords
- data
- magnetic flux
- flux leakage
- data segment
- ids
- 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.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Quality & Reliability (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Investigating Or Analyzing Materials By The Use Of Magnetic Means (AREA)
Abstract
The invention relates to a method for screening import data segments of magnetic flux leakage detection data. The method comprises the steps of (1) performing initialization: determining a proper data segment length n, a dynamic range threshold lambda delta and an energy threshold lambda E according to actual signal features; (2) reading a data segment xn, calculating a dynamic range delta (xn), and if delta (xn) is greater than or equal to lambda delta, judging that the xn is an IDS (Important Data Segment) and transmitting the xn to a compression link, otherwise, executing the step (3); and (3) calculating signal energy E(xn), if E(xn) is greater than or equal to lambda E, judging that the xn is the IDS and transmitting the xn to the compression link, and if E(xn) is less than lambda E, judging that the xn is a UDS (Unnecessary Data Segment) and only recording position and length information of the xn. A judgment policy can identify not only a magnetic flux leakage data segment with relatively large fluctuation in the segments but also a magnetic flux leakage data segment with relatively small fluctuation and relatively high signal energy in the segments, so that the possibility of omission of judgment is effectively avoided.
Description
Technical field
The present invention relates to a kind of Magnetic Flux Leakage Inspecting data significant data section screening technique.
Background technology
Magnetic Flux Leakage Inspecting curve mainly includes three kinds of data：
(1) defect of pipeline data.At defect of pipeline (as corroded, crackle, groove etc.), detection curve has higher width
Value；
(2) conduit fittingss data.(such as flange, weld seam, arm, valve, sleeve pipe etc.), detection curve at the adnexa of pipeline
Fluctuations can be also presented, but this partial trace has the form that fixation is presented, and easily which can be made with defective data
Distinguish；
(3) health data.In addition at fault location and conduit fittingss, most data are all that fluctuating very little is relatively flat
Smooth curve (generally flat after denoising), this part health data are the tube wall for representing health.
In three class data of the above, we are most concerned with defective data, and conduit fittingss data can be used to supplementary defect
Positioning, so and we be concerned about data.Accordingly, health data is then hash for us, and its
The overwhelming majority is occupied in Magnetic Flux Leakage Inspecting data, this partial redundance data is screened out and will be significantly reduced memory data output.In order to just
In narration, the Magnetic Flux Leakage Inspecting data collected at tube wall defective place, conduit fittingss are referred to as " significant data " by us；And without pipe
The Magnetic Flux Leakage Inspecting data of the intact region collection of road adnexa are referred to as " insignificant data ".
The present invention proposes a kind of to be segmented data segment importance division methods of the leakage field curve as elementary cell.The method can
Effectively to filter out important Magnetic Flux Leakage Inspecting data, screening step is simple, and will not fail to judge.
The content of the invention
It is an object of the invention to provide a kind of hash screened out in Magnetic Flux Leakage Inspecting data, retains significant data, with
The Magnetic Flux Leakage Inspecting data significant data section screening technique of memory data output is greatly reduced.
The object of the present invention is achieved like this：
Comprise the steps：
(1) initialize：According to the feature of actual signal, it is determined that suitable data sectional length n, dynamic range thresholds λΔAnd
Energy threshold λE；
(2) read data sectional xn, calculate dynamic range Δ (xn).If Δ (xn)≥λΔ, it is judged to IDS, xnSend into compression
Link, otherwise, execution step (3)；
(3) signal calculated ENERGY E (xn), if E is (xn)≥λE, it is judged to IDS, xnCompression link is sent into, if E is (xn) ＜ λE,
Then it is judged to UDS, only records xnPosition and length information.
The present invention carries out dividing with regard to the importance of data segment with equally spaced segmentation leakage field curve as elementary cell.Adopt
It is the criterion strategy based on amplitude dynamic range Δ, supplemented by signal energy E.Only when confirm the segmentation and its it is adjacent completely
During not comprising " significant data ", referred to as " insignificant data segment " (UDS)；And if the fragmented packets contain " significant data ", no matter
How much, the segmentation and its adjacent sectional are accordingly to be regarded as " significant data section " (IDS).
The beneficial effects of the present invention is：
The step of screening to simplify significant data as far as possible, the present invention propose that with equally spaced segmentation leakage field curve be basic
Unit, carries out dividing with regard to the importance of data segment.Only when confirm the segmentation and its it is adjacent completely do not include " significant data "
When, referred to as " insignificant data segment " (Unnecessary Data Segment, abbreviation UDS)；As long as and the fragmented packets contain " weight
Want data ", no matter how much, the segmentation and its adjacent sectional be accordingly to be regarded as " significant data section " (Important Data Segment,
Abbreviation IDS).For UDS, we only store the positional information numbering of the segmentation, and during decompression, the segmentation is with straight line generation that amplitude is 0
Replace；And IDS, then send into after compression link carries out data compression process and store again.
For the differentiation of IDS and UDS, the present invention proposes one kind based on amplitude dynamic range Δ, supplemented by signal energy E
Criterion strategy.The identification tactic can both recognize the magnetic flux leakage data section of big rise and fall in segmentation, can be recognized in segmentation again and be risen and fallen
The larger magnetic flux leakage data section of less but signal energy, has effectively prevented the possibility failed to judge.
Description of the drawings
Fig. 1 significant datas section differentiates schematic diagram.
The principle schematic of Fig. 2 significant datas section screening.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings：
With reference to Fig. 1.In the Magnetic Flux Leakage Inspecting data of segmentation, the present invention includes for distinguishing IDS and the signal characteristic of UDS：
Amplitude dynamic range Δ and signal energy E.It is defined as follows：
Δ(xn)=max (xn)-min(xn) \*MERGEFORMAT(1)
IDS generally has higher signal energy, larger dynamic range and amplitude change rate, and UDS is then contrary.IDS's
Dynamic range is generally larger, can be by setting suitable threshold value λΔAs judgement foundation, shown in such as Fig. 1 (a).Special feelings
Under condition, if defect is longer, its detection curve span is larger, and data sectional is located exactly at position shown in Fig. 1 (b), now, segmentation
Interior signal amplitude fluctuating is less, and only by the judgement of dynamic range Δ, what meeting was wrong gives up to fall this section of significant data, it is therefore necessary to
By signal energy E as auxiliary judgement foundation.I.e. in Δ ＜ λΔWhen, if signal energy E is higher, IDS is regarded as, here may be used
To set a suitable energy threshold λEAs judgement foundation.That is, the present invention is used based on amplitude dynamic range Δ, letter
Criterion strategy supplemented by number ENERGY E.
With reference to Fig. 2.The screening step of the significant data section of Magnetic Flux Leakage Inspecting data is：
Step 1：Initialization：According to the feature of actual signal, it is determined that suitable data sectional length n, dynamic range thresholds
λΔAnd energy threshold λE；
Step 2：Read data sectional xn, calculate dynamic range Δ (xn).If Δ (xn)≥λΔ, it is judged to IDS, xnSend into
Compression link, otherwise, execution step 3；
Step 3：Signal calculated ENERGY E (xn).If E is (xn)≥λE, it is judged to IDS, xnCompression link is sent into, if E is (xn) ＜
λE, then it is judged to UDS, only records xnPosition and length information.
Claims (1)
1. a kind of Magnetic Flux Leakage Inspecting data significant data section screening technique, it is characterised in that comprise the steps：
(1) initialize：According to the feature of actual signal, it is determined that suitable data sectional length n, dynamic range thresholds λΔAnd energy
Threshold value λE；
(2) read data sectional xn, calculate dynamic range Δ (xn).If Δ (xn)≥λΔ, it is judged to IDS, xnCompression link is sent into,
Otherwise, execution step (3)；
(3) signal calculated ENERGY E (xn), if E is (xn)≥λE, it is judged to IDS, xnCompression link is sent into, if E is (xn) ＜ λE, then sentence
It is set to UDS, only records xnPosition and length information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611042000.3A CN106528842A (en) | 2016-11-11 | 2016-11-11 | Method for screening import data segments of magnetic flux leakage detection data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611042000.3A CN106528842A (en) | 2016-11-11 | 2016-11-11 | Method for screening import data segments of magnetic flux leakage detection data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106528842A true CN106528842A (en) | 2017-03-22 |
Family
ID=58356410
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611042000.3A Pending CN106528842A (en) | 2016-11-11 | 2016-11-11 | Method for screening import data segments of magnetic flux leakage detection data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106528842A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116303296A (en) * | 2023-05-22 | 2023-06-23 | 天宇正清科技有限公司 | Data storage method, device, electronic equipment and medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103343885A (en) * | 2013-06-20 | 2013-10-09 | 西南石油大学 | Pipeline magnetic flux leakage testing on-line data compression method |
| CN103577559A (en) * | 2013-10-23 | 2014-02-12 | 华为技术有限公司 | Data ordering method and device |
| CN104282103A (en) * | 2014-10-10 | 2015-01-14 | 中国电子科技集团公司第四十一研究所 | New vibration waveform endpoint detection method based on short-time energy |
-
2016
- 2016-11-11 CN CN201611042000.3A patent/CN106528842A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103343885A (en) * | 2013-06-20 | 2013-10-09 | 西南石油大学 | Pipeline magnetic flux leakage testing on-line data compression method |
| CN103577559A (en) * | 2013-10-23 | 2014-02-12 | 华为技术有限公司 | Data ordering method and device |
| CN104282103A (en) * | 2014-10-10 | 2015-01-14 | 中国电子科技集团公司第四十一研究所 | New vibration waveform endpoint detection method based on short-time energy |
Non-Patent Citations (2)
| Title |
|---|
| 宋志强 等: "输油管道裂纹缺陷漏磁检测信号辨识分析", 《化工自动化及仪表》 * |
| 陈俊杰 等: "油气管道缺陷漏磁检测数据压缩算法研究", 《电测与仪表》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116303296A (en) * | 2023-05-22 | 2023-06-23 | 天宇正清科技有限公司 | Data storage method, device, electronic equipment and medium |
| CN116303296B (en) * | 2023-05-22 | 2023-08-25 | 天宇正清科技有限公司 | Data storage method, device, electronic equipment and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112883538B (en) | Corrosion prediction system and method for buried crude oil pipeline | |
| CN109949290A (en) | Pavement crack detection method, device, equipment and storage medium | |
| CN109492708B (en) | LS-KNN-based pipeline magnetic flux leakage internal detection missing data interpolation method | |
| CN114577418B (en) | Pressure vessel leakage detection system based on Internet of things | |
| EP2534403A2 (en) | Method of maintaining a pipeline | |
| CN103997315A (en) | Self-adaptive filtering device and method for pipeline magnetic leakage signals | |
| CN101950483B (en) | Repairing method and device for traffic data fault | |
| CN112628006A (en) | Engine intake pressure credibility detection method, device, equipment and storage medium | |
| CN106528842A (en) | Method for screening import data segments of magnetic flux leakage detection data | |
| CN113916306B (en) | Pipeline defect detection and positioning method based on multi-sensing information fusion | |
| CN116542984A (en) | Hardware defect detection method, device, computer equipment and storage medium | |
| CN105609141A (en) | Apparatus and method for automatically correcting access data of storage apparatus | |
| CN113343402B (en) | Pipeline corrosion grade evaluation method based on multilayer convolution sparse coding | |
| CN112329590A (en) | Pipeline assembly detection system and detection method | |
| WO2023125586A1 (en) | Training method and apparatus for urban underground gas leakage identification model | |
| CN110929433B (en) | Fatigue damage analysis and service life calculation method for well control device | |
| CN111767546A (en) | Deep learning-based input structure inference method and device | |
| CN116820821A (en) | Disk failure detection method, apparatus, electronic device and computer readable storage medium | |
| CN104458923B (en) | A kind ofly to detect in transformer each gas content in oil and to warn the method and system of value | |
| CN106840873A (en) | A kind of Forecasting Methodology of drilling rod residual life | |
| CN116452574A (en) | Gap detection method, system and storage medium based on improved YOLOv7 | |
| CN112528735B (en) | Automatic pipeline spiral weld flux leakage identification method and device | |
| CN110082424B (en) | Multi-scale abnormal region recommendation system and method for rapid pipeline magnetic flux leakage data | |
| CN114120043A (en) | Method for detecting abnormal pumping well based on production dynamic data and indicator diagram | |
| CN112927498A (en) | Data analysis method and device based on intelligent traffic monitoring |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170322 |