Big Data Analytics Framework for System Health Monitoring

In this paper, we present our Machine Learning (ML) based big data analytics framework that we tested to improve the quality and performance of Auxiliary Power Units (APU) health monitoring services. We are motivated to develop and apply practical and useful big data analytics technologies for industrial applications in aerospace and aviation. Key contributions of our work include the development and use of our ML algorithms that have been tested and used to analyze multiple data sources and to provide useful insights and increase the ability to predict (1) APU wear from 39%to 56% and (2) APU shutdown events from 19% to 60%. Such system health monitoring can be integrated with the widely used condition based maintenance (CBM) services. Users can use this cloud based analytic toolset and access the big data through any devices (PCs, Tablets, smart phones) anytime and anywhere.