CN108562639A - A kind of outer detection method of buried steel pipeline Life cycle defect - Google Patents

Abstract

The invention provides a method for external detection of defects in the whole life cycle of buried steel pipelines, which belongs to the technical field of non-destructive detection of pipelines. This method takes the historical data of the pipeline self-leakage magnetic field signal as a benchmark to evaluate the current pipeline defect status. It is easy to operate, low in cost and wide in scope of application. 2. Divide the pipe section section and its sub-sections; Step 3, collect the self-leakage magnetic field data when the pipeline is completed; Step 4, collect the self-leakage magnetic field data during the pipeline pressure test; Step 5, collect the self-leakage magnetic field data during the initial operation of the pipeline Step 6, collecting the self-leakage magnetic field data during the pipeline operation; Step 7, calculating the correlation degree of the pipeline self-leakage magnetic field signal; Step 8, determining the order of the severity of pipeline defects and excavating detailed inspection sub-intervals.

Description

An external detection method for defects in buried steel pipelines in the whole life cycle

technical field

The patent of the present invention relates to the field of non-destructive testing of pipelines, in particular to a non-destructive testing method for identifying, locating and sorting defects in buried steel pipelines throughout their life cycle.

Background technique

Steel pipelines have become the main mode of transportation of energy such as oil and natural gas, and are playing an increasingly important role in the national economy. In order to ensure the safe operation of pipelines, regular inspections must be carried out in order to find problems in time and take corrective measures to prevent major safety accidents and avoid huge economic losses and casualties.

At present, conventional pipeline detection methods include magnetic flux leakage detection, ultrasonic detection, ultrasonic guided wave detection, eddy current detection, magnetic particle detection, etc. Among them, magnetic flux leakage detection is the most widely used and mature, but magnetic flux leakage detection is an in-pipeline detection technology. There are certain requirements on the specifications of the pipeline, which is easy to cause jamming, and will cause magnetic pollution, which will increase the cost of magnetization and demagnetization.

In the geomagnetic field, the steel pipeline is spontaneously magnetized under the action of internal pressure, temperature stress, earth pressure and other external loads, forming a self-leakage magnetic field near the pipeline. Due to changes in the stress state, the amount of ferromagnetic materials, and the distribution of ferromagnetic materials at the defect of the pipeline, the self-leakage magnetic field is distorted. Therefore, capturing this distortion with a magnetic gradiometer allows identification and localization of defects. However, there are many problems in the application process of this method at present, mainly including:

(1) At present, the analysis of the magnetic induction intensity gradient signal of the self-leakage magnetic field obtained by the magnetic gradiometer is mainly based on human judgment, and the accuracy cannot be guaranteed;

(2) The theoretical calculation method of the self-leakage magnetic field needs to obtain basic parameters such as the magnetic characteristics of the pipeline, which is complicated to operate, high in cost, low in accuracy, and low in practicability;

(3) The current method cannot effectively judge the severity of defects, and cannot accurately identify serious defects that need to be excavated for detailed inspection and repair from a large number of pipe sections with abnormal self-leakage magnetic field gradient signals.

Based on the above analysis, there is an urgent need for a simple, applicable, and low-cost buried steel pipeline defect detection method to realize the identification, location, and severity ranking of pipeline defects, reduce human error, and improve the accuracy of serious defect identification , so as to effectively guarantee the safety of the pipeline.

Therefore, the present invention proposes a defect detection method in the whole life cycle of the pipeline based on the historical data of the self-leakage magnetic field. This method uses the historical data as the criterion for judging the current pipeline defect status through the dynamic monitoring of the pipeline's self-leakage magnetic field. This defect detection method based on historical data does not require theoretical calculation of the self-leakage magnetic field, and is simple to operate, low in cost, and widely applicable.

Contents of the invention

The invention provides a method for external detection of defects in buried steel pipelines based on historical data of self-leakage magnetic fields. Using this method can realize the functions of identification, location and classification of buried steel pipeline defects, reduce human influence, effectively identify defects that seriously affect pipeline safety, and provide guarantee for the safe operation of pipelines. The core of the external defect detection method of buried steel pipelines based on the historical data of self-leakage magnetic fields is to first collect the basic data of the pipeline and divide the pipeline into partitions, and secondly, to check and clean up the environmental factors along the pipeline that affect the data acquisition accuracy of the magnetic gradiometer ; Again, collect the gradient signal of the magnetic induction intensity gradient of the self-leakage magnetic field; then, based on the historical signal data, determine the correlation between the current self-leakage magnetic field signal and the historical signal data of each pipe section according to the current self-leakage magnetic field gradient signal collected; finally , based on the calculated correlation data, the pipelines are sorted according to the correlation from low to high, and the pipelines with serious defects are screened out for excavation and detailed inspection.

A method for external detection of defects in buried steel pipelines mainly includes the following contents:

(1) Collect the basic data of the pipeline. Basic data include pipeline design data, as-built data, routing, material, outer diameter, wall thickness, buried depth, cathodic protection device, station location, station cathodic protection location, pipeline design pressure, operating pressure, hydraulic pressure of the pipeline Slope line, pipeline accident records, pipeline inspection and maintenance records, pipeline stoppage records, and pipeline condition change records. These data constitute the basic database of the pipeline.

(2) Division of pipe sections and sub-intervals. Divide the entire pipeline into several pipe sections. The division of the pipe sections should be based on the position of the input and output of the detection pipeline, the location of the heating station and compressor station, the location of the reducing pipe, the location of the variable wall thickness, the Determine the reasonable evaluation pipe section interval based on the location of the direction, the location of the pipeline crossing structure, and the location of the pipeline cathodic protection test pile. After the pipeline partition is determined, the sub-pipe section should be determined in each pipeline section partition, and the sub-pipe section is the smallest unit of pipeline defect detection. Name the pipe interval as S, then S is the set of all pipe intervals, as shown in formula (1).

S={S ₁ , S ₂ , S ₃ ,...S _i ,...,S _m } (1)

In the formula, S is a collection of pipe section intervals, S _i is a pipe section interval, and S ₁ , S ₂ , S _{3 and} so on have a total of m pipe section intervals, which are sorted from the beginning of the pipeline to the end of the pipeline according to the pipeline mileage.

Each pipe section is composed of several sub-pipe sections, and the set of sub-pipe sections constitutes an interval, as shown in formula (2), which is the set expression of the interval S _i . The sub-pipe represents a signal data collection point in the detection.

S _i ＝{S _i1 ，S _i2 ，S _i3 ,...S _ij ,...,S _mn } (2)

In the formula, S _i is the pipe segment interval composed of each sub-interval, and the n intervals such as S _i1 , S _i2 , and S _i3 are sorted according to the pipeline mileage from the starting point of the pipe segment interval to the end point of the pipe segment interval, and S _ij is the first segment of the pipe segment interval S _i j subintervals.

(3) Collection of self-leakage magnetic field data when the pipeline is completed. After the completion of the pipeline and before the pressure test of the pipeline, the gradient signal value of the self-leakage magnetic field above the pipeline is collected along the starting point of the pipeline to the end of the pipeline. The gradient signal includes components in three directions, which are components in the x-axis direction (perpendicular to the pipeline axis direction Component), the component of the y-axis direction (the component along the axis of the pipeline) and the component of the z-axis direction (the component perpendicular to the plane where the pipeline is located). The data is organized according to the completed pipeline partitions. Each sub-interval contains three sets of data, namely, the data in the x-axis direction, y-axis direction and z-axis direction are represented by BC _ijx , BC _ijy , and BC _ijz respectively.

(4) Collection of self-leakage magnetic field data during pipeline pressure test. After the pipeline pressure test is completed, the pipeline is about to undergo pressure testing. During the pressure test, first record the pressure test pressure of the pipeline and the hydraulic gradient line of the pressure test pipe section. During the pressure test, the three-component signal of the self-leakage magnetic field magnetic induction intensity gradient of the pipeline is collected along the starting point to the end of the pressure test pipeline section. The signal also includes three parts, which are the components in the x-axis direction (components perpendicular to the pipeline axis direction) , the component in the y-axis direction (the component along the axis of the pipeline) and the component in the z-axis direction (the component perpendicular to the plane where the pipeline is located). The data is organized according to the completed pipeline partitions. Each sub-interval contains three sets of data, that is, the data in the x-axis direction, y-axis direction and z-axis direction, which are represented by BT _ijx , BT _ijy , and BT _ijz respectively.

(5) Data collection of self-leakage magnetic field during the initial operation of the pipeline. After the pressure test and rectification after the pressure test, the whole pipeline can be regarded as a defect-free pipeline. Use a three-component magnetic gradiometer to collect three-component signals of the self-leakage magnetic field magnetic induction intensity gradient of the pipeline from the beginning to the end of the pipeline. The signal also includes three parts, which are the component in the x-axis direction (the component perpendicular to the pipeline axis direction), and the y-axis direction. The component of (the component along the pipeline axis direction) and the z-axis direction component (the component perpendicular to the plane where the pipeline is located). The data is organized according to the completed pipeline partitions. Each sub-interval contains three sets of data, namely the data in the x-axis direction, the y-axis direction and the z-axis direction, which are represented by BS _ijx , BS _ijy , and BS _ijz respectively.

(6) Collect self-leakage magnetic field data during pipeline operation. During the operation of the pipeline, the self-leakage magnetic field of the pipeline is collected regularly. Before the collection work, the ferromagnetic interference along the pipeline should be removed, and then the magnetic induction intensity gradient of the self-leakage magnetic field of the pipeline is collected from the beginning to the end of the pipeline using a three-component magnetic gradiometer. The three-component signal, the signal also includes three parts, which are the component in the x-axis direction (the component perpendicular to the axis of the pipeline), the component in the y-axis direction (the component along the axis of the pipeline) and the component in the z-axis direction (perpendicular to the pipeline axis). component of the plane). The data is organized according to the completed pipeline partitions. Each sub-interval contains three sets of data, namely the data in the x-axis direction, y-axis direction and z-axis direction, which are represented by BO _ijx , BO _ijy , and BO _ijz respectively.

(7) Correlation analysis of the three-component signal of the pipeline self-leakage magnetic field gradient. During the entire life cycle of the pipeline, the defect status of the pipeline is evaluated by calculating the similarity coefficient between the current self-leakage magnetic field signal and the self-leakage magnetic field signal (historical data) obtained in the last detection. In each stage of pipeline defect assessment, the collected self-leakage magnetic field signal values are classified into pipeline sub-intervals, and the self-leakage magnetic field data of each sub-interval in the current stage and the similarity coefficient of the previous stage are obtained along the x-axis, y-axis and The calculation methods of the component values S _ix , S _iy , S _iz in the z-axis direction and the average value S _i are shown in formulas (3)-(6).

In the formula, f _i is the average similarity of the i-th subinterval of the pipeline; f _ix , f _iy , f _iz are the components of the similarity of the i-th sub-interval of the pipeline along the x-axis, y-axis and z-axis; Bp _ijx , Bp _ijy , and Bp _ijz are the components of the current self-leakage _magnetic field magnetic induction intensity gradient along the x-axis, y- _axis and z- _axis in the i-th subinterval of the pipeline; The components of the magnetic flux density gradient along the x-axis, y-axis and z-axis from the leakage field.

(8) Determine the order of severity of defects in pipe sections and excavate and inspect pipe sections in detail. According to the value of f _i , the defect status of all pipe sections (m sections in total) of the detected pipeline is sorted (from low to high). The smaller the value of pipe segment f _i is, the more serious its defect is. After the sorting is completed, first select the pipe sections ranked first and second as detailed inspection pipe sections for excavation, carry out excavation and contact inspection, and evaluate the applicability of defects according to relevant standards, if the two sub-intervals meet the applicability evaluation , then stop the detailed inspection of excavation; if at least one of the points does not meet the applicability evaluation, continue to select two relatively low-ranking points as the pipe section for detailed inspection of excavation, and repeat the above operation until two consecutive points Stop the operation when the detailed detection points meet the applicability evaluation.

Description of drawings

Accompanying drawing 1 detection method realizes the flowchart;

Accompanying drawing 2 is a schematic diagram of the relative position between the three components of the pipeline self-leakage magnetic field and the pipeline;

Accompanying drawing 3 illustrates the components along the x-axis direction of the magnetic induction intensity of the self-leakage magnetic field obtained by two detections before and after the pipeline;

Accompanying drawing 4 illustrates the component along the y-axis direction of the magnetic induction intensity of the self-leakage magnetic field obtained by two detections before and after the pipeline;

Accompanying drawing 5 illustrates the component along the z-axis direction of the self-leakage magnetic field magnetic induction intensity obtained by two detections before and after the pipeline.

Detailed ways

The specific implementation will be described in detail below with reference to the drawings and examples, so that the advantages and features of the present invention can be more easily understood by those skilled in the art, so as to define the protection scope of the present invention more clearly.

A method for external detection of defects in the entire life cycle of buried steel pipelines mainly includes eight steps, the process of which is shown in Figure 1, and the specific steps are as follows:

Step 1, collect the basic data of the pipeline. Basic data include pipeline design data, as-built data, routing, material, outer diameter, wall thickness, buried depth, cathodic protection device, station location, station cathodic protection location, pipeline design pressure, operating pressure, hydraulic pressure of the pipeline Slope line, pipeline accident records, pipeline inspection and maintenance records, pipeline stoppage records, and pipeline condition change records.

Step 2, dividing the pipe section interval and its sub-pipe sections. Divide the entire pipeline into several pipe sections. The division of pipe sections should be based on the position of the input and output of the detection pipeline, the position of the heating station and compressor station, the position of the reducing pipe, the position of changing wall thickness, the position of the valve, and the changing direction of the pipeline. The location of the pipeline, the location of the pipeline crossing structure, the location of the pipeline cathodic protection test pile and other pipeline information are reasonably determined. After the pipeline partition is determined, the sub-sections in each section should be determined, and the sub-section is the smallest unit of pipeline defect detection. The pipe section should not be greater than 100m, and the sub-pipe section should not be greater than 1m. At least one inspection point is determined for each sub-pipe section, so as to accurately locate the position of the defect in the later inspection. Name the pipe interval as S, then S is the set of all pipe intervals, as shown in formula (1).

S={S ₁ , S ₂ , S ₃ ,...S _i ,...,S _m } (7)

In the formula, S is the set composed of the intervals of each pipe section, S _i is the pipe section, S ₁ , S ₂ , S ₃ and so on have a total of m pipe sections, and they are sorted according to the pipeline mileage from the beginning of the pipeline to the end of the pipeline.

Each pipeline interval is composed of several sub-pipe sections, and the collection of sub-pipe sections constitutes a pipe section, as shown in formula (2), which is the set expression of interval S _i .

S _i ＝{S _i1 ，S _i2 ，S _i3 ,...S _ij ,...,S _mn } (8)

In the formula, S _i is the pipe segment interval composed of each sub-interval, and the n intervals such as S _i1 , S _i2 , and S _i3 are sorted according to the pipeline mileage from the beginning of the pipe segment to the end point of the pipe segment, and S _ij is the jth sub-pipe segment of the pipe segment S _i .

Step 3, collecting the self-leakage magnetic field data when the pipeline is completed. After the completion of the pipeline and before the pressure test of the pipeline, a three-component magnetic gradiometer is used to collect the three-component signal values of the magnetic induction intensity gradient of the self-leakage magnetic field above the pipeline along the pipeline starting point to the pipeline end point. Organize the data according to the completed pipeline partitions. Each sub-interval contains three sets of data respectively, that is, the data in the x-axis direction, the y-axis direction and the z-axis direction are denoted by BC _ijx , BC _ijy , and BC _ijz respectively.

Step 4, collecting the self-leakage magnetic field data during the pipeline pressure test. During the pressure test, first record the pressure test pressure of the pipeline and the hydraulic gradient line of the pressure test pipe section. During the pressure test, the ferromagnetic interference along the pipeline is removed first, and then the three-component magnetic gradient meter is used to collect the three-component signal of the self-leakage magnetic field magnetic induction intensity gradient of the pipeline from the beginning to the end of the pressure test pipeline section. Organize the data according to the completed pipeline partitions. Each sub-interval contains three sets of data respectively, that is, the data in the x-axis direction, the y-axis direction and the z-axis direction, denoted by BT _ijx , BT _ijy , and BT _ijz respectively.

Step five, collecting the self-leakage magnetic field data during the initial operation of the pipeline. Firstly, remove the ferromagnetic interference along the pipeline, and then use the three-component magnetic gradient meter to collect the three-component signal of the self-leakage magnetic field magnetic induction intensity gradient of the pipeline from the beginning to the end of the pipeline. Organize the data according to the completed pipeline partitions. Each sub-interval contains three sets of data respectively, that is, data in the x-axis direction, y-axis direction and z-axis direction, denoted by BS _ijx , BS _ijy , and BS _ijz respectively.

Step six, collecting the self-leakage magnetic field data during the operation of the pipeline. Firstly, remove the ferromagnetic interference along the pipeline, and then use the three-component magnetic gradient meter to collect the three-component signal of the self-leakage magnetic field magnetic induction intensity gradient of the pipeline from the beginning to the end of the pipeline. Organize the data according to the completed pipeline partitions. Each sub-interval contains three sets of data respectively, that is, data in the x-axis direction, y-axis direction and z-axis direction, denoted by BO _ijx , BO _ijy , and BO _ijz respectively.

Step seven, calculating the similarity coefficient of the pipeline self-leakage magnetic field signal. By calculating the similarity coefficient between the self-leakage magnetic field signal of the current pipeline and the self-leakage magnetic field signal (historical data) obtained in the last inspection, the defect status of the pipeline is evaluated, and the similarity coefficient corresponding to each sub-interval is obtained along the x-axis, y-axis and z The component values S _ix , S _iy , S _iz and the average value S _i in the axial direction are shown in formulas (3)-(6).

In the formula, f _i is the average similarity of the i-th subinterval of the pipeline; f _ix , f _iy , f _iz are the components of the similarity of the i-th sub-interval of the pipeline along the x-axis, y-axis and z-axis; Bp _ijx , Bp _ijy , and Bp _ijz are the components of the current self-leakage _magnetic field magnetic induction intensity gradient along the x-axis, y- _axis and z- _axis in the i-th subinterval of the pipeline; The components of the magnetic flux density gradient along the x-axis, y-axis and z-axis from the leakage field.

Step 8, determine the order of the severity of defects in the pipe sections and excavate and inspect the pipe sections in detail. According to the value of f _i , the defect status of all pipe sections (m sections in total) of the detected pipeline is sorted (from low to high). The smaller the value of pipe segment f _i is, the more serious its defect is. After the sorting is completed, the first and second ranked pipe sections are firstly selected as excavation detailed inspection pipe sections, and the excavation is carried out, and the defects of the pipeline are inspected in detail by means of metal magnetic memory inspection, ultrasonic inspection and ray inspection. Evaluate the applicability of defects according to the standard of the defect. If both sub-intervals meet the applicability evaluation, stop the detailed inspection of excavation; if at least one point does not meet the applicability evaluation, continue to select two points with relatively lower rankings. As the excavation detailed detection pipe section, repeat the above operation until the two consecutive excavation detailed detection points meet the applicability evaluation and stop the operation.

Below in conjunction with an experimental pipeline, apply the method of the present invention to detect the defects in its pressure test stage, so as to further elaborate the application principle of the present invention:

The first step is to collect the basic data of the pipeline according to the method described in the first step above. The diameter of the pipe is 426mm, the wall thickness is 9.5mm, and the length is 45m.

In the second step, according to the calculation method in step 2, divide the pipe section interval and its sub-pipe sections. Since the experimental pipeline is relatively short, it is divided into 6 sections, each section is 7.5m. Each pipe section is divided into 15 sub-pipe sections, and a measurement point is selected for each sub-pipe section. That is, m=6, n=15.

Step 3, collecting the self-leakage magnetic field data when the pipeline is completed. A three-component magnetic gradiometer is used to collect the three-component data of the magnetic induction intensity of the self-leakage magnetic field above the pipeline, and the three components (x-axis component, y-axis component and z-axis component) of the self-leakage magnetic field magnetic induction intensity (x-axis component, y-axis component and z-axis component) are obtained when the pipeline is completed (without internal pressure). The change curves of pipeline mileage are shown by the solid lines in Figures 3, 4 and 5, respectively.

Step 4, collecting the self-leakage magnetic field data during the pipeline pressure test. Also use the three-component magnetic gradiometer to collect the three-component data of the magnetic induction intensity of the self-leakage magnetic field above the pipeline, and obtain the three-component (x-axis component, y-axis component and z-axis component) of the self-leakage magnetic field magnetic induction intensity along the pipeline mileage during the pipeline pressure test. The change curves are shown as dotted lines in Figures 3, 4 and 5, respectively.

Since the defects in the pipeline pressure test stage are detected, the fifth and sixth steps are not required.

Step seven, calculating the similarity coefficient of the pipeline self-leakage magnetic field signal. The three-component data of the magnetic induction intensity of the self-leakage magnetic field obtained before and after the pressure test are sorted out according to the division of the pipe section, and the component values and average values of the similarity coefficient of the self-leakage magnetic field of each section along the x-axis, y-axis and z-axis are calculated.

Step 8: Sorting the defect severity of pipe sections and determining the pipe sections for detailed excavation inspection. According to the size of the average value of the similarity coefficient f _i , the defect status of all pipe sections (a total of 6 sections) of the detected pipeline is sorted (from low to high). The smaller the value of pipe segment f _i is, the more serious its defect is. The results are shown in Table 1. According to the sorting results, the pipe sections _S3 and _S1 are selected for detailed inspection. The detailed test results show that the defect condition of the pipe section meets the applicability evaluation standard. Therefore, the test ends.

Table 1 Sorting of pipe defect severity

to sort Pipe section similarity coefficient 1 S3 0.893167 2 S1 0.926192 3 S2 0.952753 4 S5 0.972155 5 S4 0.977544 6 S6 0.983419

Claims (8)

1. a kind of outer detection method of buried steel pipeline Life cycle defect, which is characterized in that the buried steel pipeline is given birth to entirely It includes mainly following eight steps to order the outer detection method of cycle defects：Step 1, the basic data of collection conduit；Step 2 is drawn It is in charge of section section and its sub- pipeline section；Step 3, natural leak magnetic field data when collection conduit is completed；Step 4, collection conduit pressure testing When natural leak magnetic field data；Step 5, natural leak magnetic field data when collection conduit initial launch；Step 6, collection conduit operation The natural leak magnetic field data of period；Step 7 calculates the pipeline natural leak magnetic field signal degree of correlation；Step 8 determines defect severity Sequence and excavation detailed inspection pipeline section.

2. step 2 as described in claim 1 divides pipeline section section and its subinterval, which is characterized in that division should be according to inspection The positions such as the input and delivery outlet position in test tube road, heating station and compressor station, the position of reducer pipe, the position for becoming wall thickness, valve Door position, the position of pipeline deflecting, pipeline have the position for wearing Oil pipeline, pipeline the moon to protect position of test pile etc. and determine reasonably Assess pipeline section section；After determining pipeline subregion, it should determine that the sub- pipeline section in subregion, subinterval are pipes in each pipeline section subregion The minimum unit of road defects detection；Pipeline section should be not more than 100m, and sub- pipeline section should be not more than 1m；Every sub- pipeline section at least determines one Test point, so that the position of defect is precisely located in late detection；Pipeline section section is named as S, then S is all pipeline section sections The set of composition, as shown in formula (1)；Each pipe section is made of several sub- pipeline sections, and the set of sub- pipeline section constitutes section, such as Shown in formula (2)：

S={ S₁, S₂, S₃,…S_i,…,S_m} (1)

S_i={ S_i1, S_i2, S_i3,…S_ij,…,S_mn} (2)

S is the set of each pipeline section section composition, S in formula_iFor pipeline section section, S₁, S₂, S₃Deng total m pipeline section section, according in pipeline Journey is ranked up from pipeline starting point to pipeline terminal；S_i1, S_i2, S_i3Deng total n section according to pipeline mileage from pipeline section section starting point To pipeline section section terminal to being ranked up, S_ijFor pipeline section section S_iJ-th of subinterval.

3. step 3 as described in claim 1, natural leak magnetic field data when collection conduit is completed, which is characterized in that collecting From before stray field, it should first remove the ferromagnetism chaff interferent along pipeline and answer；Then after pipeline completion, before pressure testing, using three points Magnetic gradiometer is measured along pipeline starting point to believe to the natural leak magnetic field magnetic induction intensity gradient three-component above pipeline terminal collection conduit Number value；Finally data are arranged according to the pipeline subregion completed, each subinterval separately includes three groups of data, i.e. x Axis direction, the data in y-axis direction and z-axis direction, uses BC respectively_ijx, BC_ijy, BC_ijzIt indicates.

4. step 4 as described in claim 1, natural leak magnetic field data when collection conduit pressure testing, which is characterized in that in pressure testing Before, it should first remove the ferromagnetism chaff interferent along pipeline；Then use three-component magnetic gradiometer along the starting point of pressure testing duct section To the natural leak magnetic field magnetic induction intensity gradient three component signal of terminal collection conduit；And according to the pipeline subregion logarithm completed According to being arranged；Each subinterval separately includes three groups of data, i.e. x-axis direction, and the data in y-axis direction and z-axis direction are used respectively BT_ijx, BT_ijy, BT_ijzIt indicates.

5. step 5 as described in claim 1, natural leak magnetic field data when collection conduit initial launch, which is characterized in that first Ferromagnetism chaff interferent along pipeline should first be removed；Then utilize three-component magnetic gradiometer along pipeline origin-to-destination collection conduit Natural leak magnetic field magnetic induction intensity gradient three component signal；Finally data are arranged according to the pipeline subregion completed, Each subinterval separately includes three groups of data, i.e. x-axis direction, and the data in y-axis direction and z-axis direction use BS respectively_ijx, BS_ijy, BS_ijzIt indicates.

6. step 6 as described in claim 1, the natural leak magnetic field data during collection conduit operation, which is characterized in that first Ferromagnetism chaff interferent along pipeline should be removed；Then utilize three-component magnetic gradiometer along pipeline origin-to-destination collection conduit Natural leak magnetic field magnetic induction intensity gradient three component signal；Finally data are arranged according to the pipeline subregion completed, often A subinterval separately includes three groups of data, i.e. x-axis direction, and the data in y-axis direction and z-axis direction use BO respectively_ijx, BO_ijy, BO_ijzIt indicates.

7. step 7 as described in claim 1 calculates the pipeline natural leak magnetic field signal degree of correlation, which is characterized in that will be collected into Natural leak Field signal value classify by pipeline subinterval, and calculate each subinterval pressure testing stage according to formula (3)~(6) With the similarities of completed papers along the component value S in x-axis, y-axis and z-axis direction_ix, S_iy, S_izAnd average value S_i：

F in formula_iFor the similarity average value in the i-th subinterval of pipeline；f_ix, f_iy, f_izFor the i-th subinterval of pipeline similarity along x-axis, The component of y-axis and z-axis；Bp_ijx, Bp_ijyAnd Bp_ijzIt is the current natural leak magnetic field magnetic induction intensity gradient in the i-th subinterval of pipeline along x The component of axis, y-axis and z-axis；Bl_ijx, Bl_ijyAnd Bl_ijzNatural leak magnetic field magnetic induction when being detected for pipeline the i-th subinterval last time Intensity gradient is along x-axis, the component of y-axis and z-axis.

8. step 8 as described in claim 1 determines the sequence of pipeline section defect severity and excavates detailed inspection pipeline section, special Sign is, according to f_iValue size, to detect pipeline all pipeline sections (m sections total) defect condition be ranked up (by as low as It is high)；Pipeline section f_iValue it is smaller, defect level is more serious；After the completion of sequence, the pipeline section work to rank the first with second is chosen first Pipeline section is detected in detail to excavate, and is excavated and contacted detection, and evaluate the applicability of defect according to relevant standard, If two subintervals all meet fitness-for-service assessment, stop excavating detailed detection；If wherein at least one point is unsatisfactory for being applicable in Property evaluation, then continue to choose ranking two points relatively rearward as the pipeline section detected in detail is excavated, repeatedly aforesaid operations, until Two continuously taken, which excavate when detailed test point meets fitness-for-service assessment, stops the operation.