Machine Learning Techniques for Automatic Sensor Fault Detection in HUMS Systems
|Title:||Machine Learning Techniques for Automatic Sensor Fault Detection in HUMS Systems||Authors:||Melia, Thomas; Cooke, Alan; Grayson, 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 & Statistics; Sensors; Self-diagnostics; Machine-learning; Instrumentation; Built-in-test||Other versions:||http://aiac17.com/
|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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