Declan Mallamo, Michael H. Azarian, and Michael G. Pecht
University of Maryland, College Park, MD 20742 USA
For more information about this article and related research, please contact Dr. Michael H. Azarian.
This study introduces the Elastic Sparse Functional k-Nearest Neighbors approach, a predictive health monitoring framework specifically tailored for turbofan engines. This method begins by transforming time-series data into a standardized universal flight domain, which is further optimized through elastic registration for alignment across varying flight regimes. Standard scaling is employed as a preprocessing step, setting the stage for feature dimensionality reduction via Functional Principal Components Analysis. To pinpoint the features that most significantly impact engine health, the method leverages Orthogonal Matching Pursuit in conjunction with k-Nearest Neighbors to build a sparse regression model. The model's performance is assessed using root mean square error on test cases derived from the NCMAPSS DS02 dataset. Recommendations are given based on the interpretive results relating to targeting data collection and formulating hypotheses for root cause analysis.
The article is available for free online here.