• norsk
    • English
  • norsk 
    • norsk
    • English
  • Logg inn
Vis innførsel 
  •   Hjem
  • Fakultet for ingeniørvitenskap (IV)
  • Institutt for marin teknikk
  • Vis innførsel
  •   Hjem
  • Fakultet for ingeniørvitenskap (IV)
  • Institutt for marin teknikk
  • Vis innførsel
JavaScript is disabled for your browser. Some features of this site may not work without it.

Particle Filter for Fault Diagnosis: Applications to Dynamic Positioning Vessels and Underwater Robotics

Zhao, Bo
Doctoral thesis
Thumbnail
Åpne
Fulltext (29.60Mb)
Permanent lenke
http://hdl.handle.net/11250/296033
Utgivelsesdato
2015
Metadata
Vis full innførsel
Samlinger
  • Institutt for marin teknikk [2351]
Sammendrag
Safety remains an important consideration in control system design. This is particularly

true for the control systems that are employed by the offshore petroleum and

maritime industries, in which authorities continually claim to have developed more

and more rigorous methods and processes to ensure safety and reliability. In addition

to the use of more traditional approaches to conducting redundancy design, backup

system design, robust design, and failure mode and effect analysis, these methods often

involve the use of fault tolerant design to enhance the safety and reliability of the

control system.

As a common practice, segregation and redundant design are used for dynamic positioning

vessels to isolate faulty components and prevent the propagation of faults.

However, many incidents still occur as a result of fault escapes from the segregation

on a torpid detection. In more severe cases, false detection can actually cause faults

and may result in an even more dangerous situation that has more catastrophic consequences.

Hence, precise and timely fault diagnosis is necessary for the operator or the

automation system to take appropriate action.

This thesis presents a brief overview of the existing fault diagnose methods, with a

particular focus on particle-filter-based framework for fault diagnosis. The paper commences

with a brief review of the background, theory, and typical features of the particle

filter before progressing to examine the relationships and differences between the

particle filter and other traditional stochastic filters. Switching mode hidden Markov

model were employed to model a system with potential faults and a new methodology

that uses a particle filter as fault diagnosis filter was developed. This method was

then applied on an underwater robotic, which worked in complex environmental disturbance

and suffered from different failure modes. Experimental results from ROV

sea trails verified that the new fault diagnosis design is effective and reliable.
Utgiver
NTNU
Serie
Doctoral thesis at NTNU;2015:160

Kontakt oss | Gi tilbakemelding

Personvernerklæring
DSpace software copyright © 2002-2019  DuraSpace

Levert av  Unit
 

 

Bla i

Hele arkivetDelarkiv og samlingerUtgivelsesdatoForfattereTitlerEmneordDokumenttyperTidsskrifterDenne samlingenUtgivelsesdatoForfattereTitlerEmneordDokumenttyperTidsskrifter

Min side

Logg inn

Statistikk

Besøksstatistikk

Kontakt oss | Gi tilbakemelding

Personvernerklæring
DSpace software copyright © 2002-2019  DuraSpace

Levert av  Unit