Version 1
: Received: 17 October 2019 / Approved: 18 October 2019 / Online: 18 October 2019 (11:29:49 CEST)
How to cite:
Khan, F. Online Subspace Tracking of Sensors Data for Damage Propagation Modeling and Predictive Analytics. Preprints2019, 2019100212. https://doi.org/10.20944/preprints201910.0212.v1
Khan, F. Online Subspace Tracking of Sensors Data for Damage Propagation Modeling and Predictive Analytics. Preprints 2019, 2019100212. https://doi.org/10.20944/preprints201910.0212.v1
Khan, F. Online Subspace Tracking of Sensors Data for Damage Propagation Modeling and Predictive Analytics. Preprints2019, 2019100212. https://doi.org/10.20944/preprints201910.0212.v1
APA Style
Khan, F. (2019). Online Subspace Tracking of Sensors Data for Damage Propagation Modeling and Predictive Analytics. Preprints. https://doi.org/10.20944/preprints201910.0212.v1
Chicago/Turabian Style
Khan, F. 2019 "Online Subspace Tracking of Sensors Data for Damage Propagation Modeling and Predictive Analytics" Preprints. https://doi.org/10.20944/preprints201910.0212.v1
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.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.