Model-based Sensor-Fault Detection and Isolation in Natural-Gas Pipelines for Transient Flow

被引：1

作者：

Shaheen, Khadija ^{[1
]}

Chawla, Apoorva ^{[1
]}

Uilhoorn, Ferdinand Evert ^{[2
]}

Rossi, Pierluigi Salvo ^{[1
]}

机构：

[1] Norwegian Univ Sci & Technol, Dept Elect Syst, Trondheim, Norway

[2] Warsaw Univ Technol, Dept Gas Engn, Warsaw, Poland

来源：

2023 IEEE SENSORS | 2023年

关键词：

Data fusion; fault detection and isolation; partial differential equations; unscented Kalman filter; transient flow;

D O I：

10.1109/SENSORS56945.2023.10325151

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Sensor-based monitoring of natural gas pipelines is crucial for safety and detecting sensor faults is pivotal for reliable operations. This paper investigates the problem of fault diagnosis in a natural gas pipeline under transient flow, characterized by a system of hyperbolic partial differential equations (PDEs). A data-fusion approach based on an unscented Kalman filter (UKF) is employed to perform sensor-fault detection and isolation (SFDI). The architecture consists of a bank of local UKFs to provide the state estimates, which are further analyzed to identify sensor faults. The performance achieved by the proposed method is promising even in the case of a nonlinear flow model.

引用

页数：4

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