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 language | English |
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Title of host publication | OCEANS 2016 MTS/IEEE Monterey |
Publisher | IEEE |
ISBN (Electronic) | 9781509015375 |
DOIs | |
Publication status | Published - 1 Dec 2016 |
Event | 2016 OCEANS MTS/IEEE Monterey - Monterey, United States Duration: 19 Sept 2016 → 23 Sept 2016 |
Conference
Conference | 2016 OCEANS MTS/IEEE Monterey |
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Country/Territory | United States |
City | Monterey |
Period | 19/09/16 → 23/09/16 |
Keywords
- Fault detection and diagnosis
- Gaussian mixtures
- Hidden markov models
- Underwater long-term autonomy
ASJC Scopus subject areas
- Instrumentation
- Oceanography
- Ocean Engineering