Online Subspace Tracking for Damage Propagation Modeling and Predictive Analytics: Big Data Perspective

TL;DR

This paper presents a data-driven approach for damage propagation modeling in turbo-engines using subspace tracking on big data, enabling effective condition-based maintenance and remaining useful life estimation.

Contribution

It introduces a novel algorithm leveraging low-dimensional manifold structures for damage modeling, improving computational efficiency and performance over existing methods.

Findings

Significant performance improvements on CMAPSS datasets.

Effective damage propagation modeling using subspace tracking.

Enhanced RUL estimation accuracy.

Abstract

We analyze damage propagation modeling of turbo-engines in a data-driven approach. We investigate subspace tracking assuming a low dimensional manifold structure and a static behavior during the healthy state of the machines. Our damage propagation model is based on the deviation of the data from the static behavior and uses the notion of health index as a measure of the condition. Hence, we incorporate condition-based maintenance and estimate the remaining useful life based on the current and previous health indexes. This paper proposes an algorithm that adapts well to the dynamics of the data and underlying system, and reduces the computational complexity by utilizing the low dimensional manifold structure of the data. A significant performance improvement is demonstrated over existing methods by using the proposed algorithm on CMAPSS Turbo-engine datasets.