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Research Papers

Pairwise Critical Point Detection Using Torque Signals in Threaded Pipe Connection Processes

[+] Author and Article Information
Juan Du

Department of Industrial Engineering
and Management,
Peking University,
Beijing 100871, China
e-mail: dujuan@pku.edu.cn

Xi Zhang

Department of Industrial Engineering
and Management,
Peking University,
Beijing 100871, China
e-mail: xi.zhang@pku.edu.cn

Jianjun Shi

Fellow ASME
H. Milton Stewart School of Industrial
and Systems Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: jianjun.shi@isye.gatech.edu

1Corresponding author.

Manuscript received November 6, 2016; final manuscript received May 26, 2017; published online June 22, 2017. Assoc. Editor: Satish Bukkapatnam.

J. Manuf. Sci. Eng 139(9), 091002 (Jun 22, 2017) (11 pages) Paper No: MANU-16-1585; doi: 10.1115/1.4036992 History: Received November 06, 2016; Revised May 26, 2017

The quality of threaded pipe connections is one of the key quality characteristics of drill pipes, risers, and pipelines. This quality characteristic is evaluated mainly by a pair of critical points, which are corresponding to the mechanical deformations formed in the pipe connection process. However, these points are difficult to detect because of nonlinear patterns generated by latent process factors in torque signals, which conceal the true critical points. To address this problem, we propose a novel three-phase state-space model that incorporates physical interpretations of connection process to detect pairwise critical points. We also develop a two-stage recursive particle filter to estimate the locations of the underlying critical points. Results of a real threaded pipe connection case show that the detection performance of the proposed method is more powerful than that of other existing methods.

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References

Figures

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Fig. 1

Coupling screw-on-machine

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Fig. 2

Torque signals collected in the threaded pipe connection process

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Fig. 3

Cross section of the threaded pipe connection

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Fig. 4

Overview of the proposed approach

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Fig. 5

Nominal torque curve during the pipe connection process

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Fig. 6

The structure schematic of the connection: (a) the schematic of the thread engagement and (b) the schematic of sealing structure

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Fig. 7

The flowchart of the first stage particle filter algorithm

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Fig. 8

A demonstration of pairwise point detection and slope change in the pipe connection process

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Fig. 9

Examples of pairwise point detection in torque signals with common nonlinear profiles

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Fig. 10

Comparisons between piecewise linear regression model (left panel) and the proposed model (right panel) for pairwise critical point detection

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