To main content

Bayesian Fault Detection and Localization Through Wireless Sensor Networks in Industrial Plants

Abstract

This work proposes a data fusion approach for quickest fault detection and localization within industrial plants via wireless sensor networks. Two approaches are proposed, each exploiting different network architectures. In the first approach, multiple sensors monitor a plant section and individually report their local decisions to a fusion center. The fusion center provides a global decision after spatial aggregation of the local decisions. A post-processing center subsequently processes these global decisions in time, which performs quick detection and localization. Alternatively, the fusion center directly performs a spatio-temporal aggregation directed at quickest detection, together with a possible estimation of the faulty item. Both architectures are provided with a feedback system where the network’s highest hierarchical level transmits parameters to the lower levels. The two proposed approaches model the faults according to a Bayesian criterion and exploit the knowledge of the reliability model of the plant under monitoring. Moreover, adaptations of the well-known Shewhart and CUSUM charts are provided to fit the different architectures and are used for comparison purposes. Finally, the algorithms are tested via simulation on an active Oil and Gas subsea production system, and performances are provided.
Read publication

Category

Academic article

Language

English

Author(s)

Affiliation

  • Norwegian University of Science and Technology
  • SINTEF Energy Research / Gassteknologi
  • University of Naples 'Federico II'

Year

2024

Published in

IEEE Internet of Things Journal

ISSN

2327-4662

Publisher

IEEE (Institute of Electrical and Electronics Engineers)

Volume

11

Issue

8

Page(s)

13231 - 13246

View this publication at Cristin