Online Subspace Tracking for Damage Propagation Modeling and Predictive Analytics: Big Data Perspective
This work addresses predictive maintenance for turbo-engines, offering an incremental improvement in computational efficiency and accuracy.
The paper tackled damage propagation modeling for turbo-engines by proposing an online subspace tracking algorithm that leverages low-dimensional manifold structures, resulting in significant performance improvements over existing methods on CMAPSS datasets.
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.