Competing risks survival analysis of ruptured gas pipelines: A nonparametric predictive approach

被引:1
|
作者
Tee, Kong Fah [1 ]
Pesinis, Konstantinos [1 ]
Coolen-Maturi, Tahani [2 ]
机构
[1] Univ Greenwich, Sch Engn, Chatham, Kent, England
[2] Univ Durham, Dept Math Sci, Durham, England
关键词
Gas pipelines; Historical failure data; Nonparametric predictive inference; Competing risks; Rupture; RELIABILITY-ANALYSIS; INFERENCE; MAINTENANCE; PROBABILITY; INCIDENTS; SYSTEM; SOIL;
D O I
10.1016/j.ijpvp.2019.06.001
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Risk analysis based on historical failure data can form an integral part of the integrity management of oil and gas pipelines. The scarcity and lack of consistency in the information provided by major incident databases leads to non-specific results of the risk status of pipes under consideration. In order to evaluate pipeline failure rates, the rate of occurrence of failures is commonly adopted. This study aims to derive inductive inferences from the 179 reported ruptures of a set of onshore gas transmission pipelines, reported in the PHMSA database for the period from 2002 to 2014. Failure causes are grouped in an integrated manner and the impact of each group in the probability of rupture is examined. Towards this, nonparametric predictive inference (NPI) is employed for competing risks survival analysis. This method provides interval probabilities, also known as imprecise reliability, in that probabilities and survival functions are quantified via upper and lower bounds. The focus is on a future pipe component (segment) that ruptures due to a specific failure cause among a range of competing risks. The results can be used to examine and implement optimal maintenance strategies based on relative risk prioritization.
引用
收藏
页数:10
相关论文
共 50 条
  • [1] Nonparametric predictive inference for competing risks
    Maturi, T. A.
    Coolen-Schrijner, P.
    Coolen, F. P. A.
    [J]. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY, 2010, 224 (O1) : 11 - 26
  • [2] Nonparametric predictive pairwise comparison with competing risks
    Coolen-Maturi, Tahani
    [J]. RELIABILITY ENGINEERING & SYSTEM SAFETY, 2014, 132 : 146 - 153
  • [3] Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks
    Zhang, Quan
    Zhou, Mingyuan
    [J]. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), 2018, 31
  • [4] A Predictive Approach to Bayesian Nonparametric Survival Analysis
    Fong, Edwin
    Lehmann, Brieuc
    [J]. INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 151, 2022, 151
  • [5] Nonparametric predictive inference for combined competing risks data
    Coolen-Maturi, Tahani
    Coolen, Frank P. A.
    [J]. RELIABILITY ENGINEERING & SYSTEM SAFETY, 2014, 126 : 87 - 97
  • [6] Nonparametric Predictive Multiple Comparisons with Censored Data and Competing Risks
    Maturi, Tahani A.
    Coolen-Schrijner, Pauline
    Coolen, Frank P. A.
    [J]. ISIPTA '09: PROCEEDINGS OF THE SIXTH INTERNATIONAL SYMPOSIUM ON IMPRECISE PROBABILITY: THEORIES AND APPLICATIONS, 2009, : 307 - 316
  • [7] A nonparametric instrumental approach to confounding in competing risks models
    Beyhum, Jad
    Florens, Jean-Pierre
    Van Keilegom, Ingrid
    [J]. LIFETIME DATA ANALYSIS, 2023, 29 (04) : 709 - 734
  • [8] A nonparametric instrumental approach to confounding in competing risks models
    Jad Beyhum
    Jean-Pierre Florens
    Ingrid Van Keilegom
    [J]. Lifetime Data Analysis, 2023, 29 : 709 - 734
  • [9] DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks
    Lee, Changhee
    Zame, William R.
    Yoon, Jinsung
    van der Schaar, Mihaela
    [J]. THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2018, : 2314 - 2321
  • [10] Nonparametric Analysis of Bivariate Gap Time with Competing Risks
    Huang, Chiung-Yu
    Wang, Chenguang
    Wang, Mei-Cheng
    [J]. BIOMETRICS, 2016, 72 (03) : 780 - 790