Method for automatically detecting key points for casing collars of steel pipes

A casing coupling and automatic detection technology, applied in the field of quality engineering, can solve the problems of outflow, loss, oil leakage, etc.

Active Publication Date: 2017-03-22
PEKING UNIV
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  • Abstract
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  • Claims
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Problems solved by technology

However, the detection of the sealing point and the shoulder point is equally important. Failure to detect the sealing point may lead to outflow of the poorly sealed casing collar connection, resulting in oil leakage and huge losses

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  • Method for automatically detecting key points for casing collars of steel pipes
  • Method for automatically detecting key points for casing collars of steel pipes
  • Method for automatically detecting key points for casing collars of steel pipes

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Embodiment Construction

[0064] Below in conjunction with accompanying drawing, further describe the present invention through embodiment, but do not limit the scope of the present invention in any way.

[0065] The invention provides a key point detection method for casing collar connection, establishes a two-stage model framework, and proposes an advanced statistical method using positive and negative sequences to detect two key explainable change points. The method described is based on the torque signal of a casing collar connection with a sealing surface: first based on a dynamic time warping method to measure the similarity of the torque signal and an interpretable physical basis. Then, in the first stage, all potential change points in the torque signal are detected based on the statistical test of F-maximum and the two-phase regression model. In the second stage, two more likely key change points are selected through the screening of reverse order change points based on physical constraints. ...

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Abstract

The invention discloses a method for automatically detecting key points for casing collars of steel pipes; a two-stage model is established based on a torque signal connected to a casing collar with seal surface, all potential varying points in the torque signal are detected in the first stage, and physical basis is generated according to the torque signal specifically; a two-phase regression model is provided to acquire all possible varying points; shoulder point and seal point distance restraints are acquired in the second stage, varying points in a varying point set are screened to select more possible key varying points; seal points and shoulder points are determined based on a weighted three-phase regression model. The technical scheme for monitoring special fastener screwing quality automatically and precisely is provided, the automated monitoring process of casing collar connection quality can be quickened, labor cost of manual observation for positions of seal points and shoulder points is saved, quality monitoring false alarm rate and scrappage rate are decreased.

Description

technical field [0001] The invention belongs to the field of quality engineering and relates to the quality of casing collar connection engineering, in particular to an automatic detection method for obtaining key points of steel pipe casing collar connection based on forward detection and backward screening of torque signals. Background technique [0002] Tightness is one of the key quality characteristics of petroleum pipelines. In the current process of casing coupling screwing, manual detection by experienced engineers is adopted, not only the accuracy of measurement cannot be guaranteed, but also there may be negligence in the work that may lead to the outflow of waste products, so automatic detection of casing The tightness of the coupling is very necessary. During the casing collar screwing process, this quality characteristic mainly relies on two key interpretable change points in the torque signal collected by the sensor installed on the screwing machine. In this ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N19/04G06F17/18
CPCG01N19/04G06F17/18
Inventor 杜娟张玺
Owner PEKING UNIV
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