A Hidden Markov Model application with Gaussian Mixture emissions for fault detection and diagnosis on a simulated AUV platform

Mariela De Lucas Alvarez, David M. Lane

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

This paper presents an application of a Hidden Markov Model for fault detection and diagnosis on a testbed that emulates an AUV thruster system. The testbed consists in circuit board with two DC motors that represent the thrusters and embedded features to produce malfunctions. We present how the model is learned using the Expectation Maximization algorithm for Gaussian Mixtures and how the testbed is monitored probabilistic inference. Diagnosis is also performed using GMM classifiers. We describe how the framework deals with non-Gaussian data and how it reflects in the accuracy overall.

Original languageEnglish
Title of host publicationOCEANS 2016 MTS/IEEE Monterey
PublisherIEEE
ISBN (Electronic)9781509015375
DOIs
Publication statusPublished - 1 Dec 2016
Event2016 OCEANS MTS/IEEE Monterey - Monterey, United States
Duration: 19 Sep 201623 Sep 2016

Conference

Conference2016 OCEANS MTS/IEEE Monterey
CountryUnited States
CityMonterey
Period19/09/1623/09/16

Keywords

  • Fault detection and diagnosis
  • Gaussian mixtures
  • Hidden markov models
  • Underwater long-term autonomy

ASJC Scopus subject areas

  • Instrumentation
  • Oceanography
  • Ocean Engineering

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