NDLI: Evolutionary Tuning of Non-parametric Information Conversion Functions for Diagnostics

Content Provider	IEEE Xplore Digital Library
Author	Xiao Hu Kai Goebel Eklund, N.
Copyright Year	2006
Description	Author affiliation: GE Global Res., Niskayuna, NY (Xiao Hu; Kai Goebel; Eklund, N.)
Abstract	In aircraft health management applications, there are two types of data available, parametric and non-parametric data. Parametric data, such as sensor measurements from engine or airframe, have traditionally been the primary data source for proactive diagnostic and performance monitoring applications because they allow one to trend even subtle changes over time. Non-parametric data (e.g., error logs and fault messages, that are either of textual nature or binary flags) are more often used for event driven maintenance. Fundamentally, both information sources harbor diagnostic information that, when used together, might improve results for both applications. This paper employs a conversion function to parameterize non-parametric information so that it can be used together with parametric data to enhance the diagnosis of aircraft engine faults. Genetic algorithms are applied for fine-tuning of parameters of the conversion function. Experimental results from real engine data are presented
Starting Page	476
Ending Page	482
File Size	3348682
Page Count	7
File Format	PDF
ISBN	7302139229
DOI	10.1109/CESA.2006.4281699
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-10-04
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	IMACS
Subject Keyword	information fusion Time measurement genetic algorithms Aircraft propulsion Engines Petroleum message decaying Genetic algorithms Aerospace engineering Fault diagnosis Condition monitoring Temperature sensors soft performance metric diagnostics Fault detection
Content Type	Text
Resource Type	Article

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