Remaining Useful Life Estimation of Cooling Units via Time-Frequency Health Indicators with Machine Learning

Aerospace

Bernardete Ribeiro¹  Raúl Llasag Rosero¹  Catarina Silva¹ 

[1] CISUC—Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Polo II—Pinhal de Marrocos, University of Coimbra, 3030-290 Coimbra, Portugal;

关键词: predictive maintenance;    prognostics and health maintenance;    remaining useful life;    health indicators;    machine learning;    artificial intelligence;   

DOI  :  10.3390/aerospace9060309

来源: DOAJ

【 摘 要 】

Predictive Maintenance (PM) strategies have gained interest in the aviation industry to reduce maintenance costs and Aircraft On Ground (AOG) time. Taking advantage of condition monitoring data from aircraft systems, Prognostics and Health Maintenance (PHM) practitioners have been predicting the life span of aircraft components by applying Remaining Useful Life (RUL) concepts. Additionally, in prognostics, the construction of Health Indicators (HIs) plays a significant role when failure advent patterns are strenuous to be discovered directly from data. HIs are typically supported by data-driven models dealing with non-stationary signals, e.g., aircraft sensor time-series, in which data transformations from time and frequency domains are required. In this paper, we build time-frequency HIs based on the construction of the Hilbert spectrum and propose the integration of a physics-based model with a data-driven model to predict the RUL of aircraft cooling units. Using data from a major airline, and considering two health degradation stages, the advent of failures on aircraft systems can be estimated with data-driven Machine Learning models (ML). Specifically, our results reveal that the analyzed cooling units experience a normal degradation stage before an abnormal degradation that emerges within the last flight hours of useful life.

【 授权许可】

Unknown