Case Studies: Big Data Analytics for System Health Monitoring

摘要

This paper describes a case-study where we built and exercised a cloud computing framework with machine learning (ML) algorithms to improve the accuracy of Auxiliary Power Units (APU) health monitoring. An APU is a small turbo machine that flies on all commercial transport airplanes. The paper describes the objective of our study, sources of available data, the ETL scripts to populate the underlying HBase tables and two examples. In one example machine learning algorithms operating on multiple data sources produce useful insights to increase our ability to predict APU wear from 39% to 56%. In the second example, it increased our ability to predict shutdown events from 19% to 60%. This case-study illustrates the effectiveness of big data analytics and tools to discover additional insights that can further reduce operational interrupts arising from airborne equipment problems.