Machine Learning Techniques for Automatic Sensor Fault Detection in HUMS Systems

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Title: Machine Learning Techniques for Automatic Sensor Fault Detection in HUMS Systems
Authors: Melia, ThomasCooke, AlanGrayson, Siobhán
Permanent link: http://hdl.handle.net/10197/9023
Date: 28-Feb-2017
Online since: 2017-11-01T11:53:57Z
Abstract: In this paper we describe the problem of developing sensor fault detection within HUMS instrumentation systems, and solutions based upon machine-learning techniques. We conclude with a report on our proof-of-concept demonstrator, and outline next-steps towards implementation of a autonomous self diagnostic sensor solution.
Funding Details: Science Foundation Ireland
Funding Details: Insight Research Centre
Type of material: Conference Publication
Publisher: Engineers Australia
Keywords: Machine Learning & StatisticsSensorsSelf-diagnosticsMachine-learningInstrumentationBuilt-in-test
Other versions: http://aiac17.com/
http://search.informit.com.au/documentSummary;dn=740752216129676;res=IELENG
Language: en
Status of Item: Peer reviewed
Conference Details: 17th Australian International Aerospace Congress: AIAC 2017, Melbourne
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Insight Research Collection

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