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We analyze damage propagation modeling of turbo-engines in a data-drivenapproach. We investigate subspace tracking assuming a low dimensional manifoldstructure and a static behavior during the healthy state of the machines. Ourdamage propagation model is based on the deviation of the data from the staticbehavior and uses the notion of health index as a measure of the condition.Hence, we incorporate condition-based maintenance and estimate the remaininguseful life based on the current and previous health indexes. This paperproposes an algorithm that adapts well to the dynamics of the data andunderlying system, and reduces the computational complexity by utilizing thelow dimensional manifold structure of the data. A significant performanceimprovement is demonstrated over existing methods by using the proposedalgorithm on CMAPSS Turbo-engine datasets.

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