Sequential pattern mining applied to aeroengine condition monitoring with uncertain health data

TitleSequential pattern mining applied to aeroengine condition monitoring with uncertain health data
Publication TypeJournal Article
Year of Publication2015
AuthorsPalacios, Ana, Martínez Alvaro, Sánchez Luciano, and Couso Inés
JournalEngineering Applications of Artificial Intelligence
Volume44
Pagination10 - 24
ISSN0952-1976
Keywordsengine health monitoring, Fuzzy rule-based systems, Fuzzy sequence mining, Uncertain data
Abstract

Numerical algorithms that can assess Engine Health Monitoring (EHM) data in aeroengines are influenced by the high level of uncertainty inherent to gas path measurements and engine-to-engine variability. Among them, fuzzy rule-based techniques have been successfully used due to their robustness towards noisy signals and their capability to learn human-readable rules from data. These techniques are useful in detecting the presence of certain types of abnormal events or general engine deterioration, through the identification of specific combinations of EHM signals associated with these specific cases. However, there are also other types of engine events that manifest themselves as an ordered sequence of otherwise normal combinations of the EHM signals. These combinations are dismissed when considered in isolation as the current existing techniques cannot assess them. In this paper it is proposed to use sequence mining techniques in order to obtain fuzzy rules from uncertain EHM data which can in turn be used to identify the cases where an engine event is determined as a sequence of otherwise normal combinations of EHM signals. The results are subsequently tested on a representative sample of aeroengine data.

URLhttp://www.sciencedirect.com/science/article/pii/S0952197615001062
DOI10.1016/j.engappai.2015.05.003