Predictive Maintenance using Functional Data Analysis

C. Yildirim, A. M. Franco Pereira, R. E. Lillo Rodriguez

In this work, a practical reliability analysis and engine health prognostic study is performed using Functional Data Analysis. Multi-sensor data collected from aircraft engines are processed in order to solve one of the most important reliability analysis problems which is estimating the health condition and remaining useful life of an aircraft engine. Smooth sensor curves obtained from different informative sensors are processed using Multivariate Functional Principal Component Analysis to develop a predictive machine learning model and estimate upcoming failures before the failure occurs. Distribution of the principal component scores provided us to understand sensor behavior and suggests a differentiation of different types of machines based on qualitative variables.

Keywords: FDA, MFPCA, Predictive Maintenance, Reliability Analysis, Engine Prognostic

Scheduled

GT06 Functional Data Analysis I. New methodologies
June 8, 2022  12:40 PM
Grade Hall

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