eprintid: 16350
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/63/50
datestamp: 2023-12-19 03:22:53
lastmod: 2023-12-19 03:22:53
status_changed: 2023-12-19 03:06:05
type: article
metadata_visibility: show
creators_name: Mujtaba, S.M.
creators_name: Lemma, T.A.
creators_name: Vandrangi, S.K.
title: Leak diagnostics in natural gas pipelines using fault signatures
ispublished: pub
keywords: Corrosion rate; Energy resources; Fault detection; Flow rate; Leak detection; Natural gas; Natural gas pipelines; Natural gas transportation; Petroleum deposits; Pipeline corrosion; White noise, Adaptive thresholds; Diagnostic matrix; Fault signature; Leaks detections; matrix; Model identification; Oil and natural gas; OLGA simulator; Pipeline leak detection; Pipeline leaks, Mass transfer
note: cited By 1
abstract: Most of the oil and natural gas resources are transported via pipelines. However, due to unavoidable factors such as corrosion and earthquakes, these pipelines frequently experience faults such as leaks. In the past, undetected leaks in pipelines resulted in massive human and material losses. Though, it is possible to timely and accurately detect leaks or other faults in pipelines by improvising existing fault detection and diagnostics (FDD) methodologies. In this study, fault signatures are used to identify a leakage as well as a leaking section in a natural gas pipeline. A long transportation pipeline (up to 150 km) is simulated under transient conditions for the leak detection and diagnostics (LDD) study. Under normal operating conditions, mass flow rate measurements are used to estimate pipeline models based on autoregressive exogenous (ARX) model. Mass flow rate limits under leak-free conditions are defined by calculating adaptive thresholds. The models are tested for leakage at several locations; a minimum detectable leak with zero false alarm was 0.084 m in diameter (around 6 of the total diameter). Finally, the indicated leakage started the algorithm to identify the leaking section. Identification of a leaking section is based on a fault signature from three locations in a pipeline. The leaking section was detected by comparing a specific fault signature with a defined diagnostics matrix in the presence of 0.5 white noise. © 2022 Elsevier Ltd
date: 2022
publisher: Elsevier Ltd
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130777874&doi=10.1016%2fj.ijpvp.2022.104698&partnerID=40&md5=18ce1ebb33ee201a046bde621e6ba4bf
id_number: 10.1016/j.ijpvp.2022.104698
full_text_status: none
publication: International Journal of Pressure Vessels and Piping
volume: 199
refereed: TRUE
issn: 03080161
citation:   Mujtaba, S.M. and Lemma, T.A. and Vandrangi, S.K.  (2022) Leak diagnostics in natural gas pipelines using fault signatures.  International Journal of Pressure Vessels and Piping, 199.   ISSN 03080161