Scalable prognostic models for large-scale condition monitoring applications

Abstract

ABSTRACTHigh-value engineering assets are often embedded with numerous sensing technologies that monitor and track their performance. Capturing physical and performance degradation entails the use of various types of sensors that generate massive amounts of multivariate data. Building a prognostic model for such large-scale datasets, however, often presents two key challenges: how to effectively fuse the degradation signals from a large number of sensors and how to make the model scalable to the large data size. To address the two challenges, this article presents a scalable semi-parametric statistical framework specifically designed for synthesizing and combining multistream sensor signals using two signal fusion algorithms developed from functional principal component analysis. Using the algorithms, we identify fused signal features and predict (in near real-time) the remaining lifetime of partially degraded systems using an adaptive functional (log)-location-scale regression modeling framework. We validate the proposed multi-sensor prognostic methodology using numerical and data-driven case studies.