A Survey of Data -Driven Prognostics

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A review of diagnostic and prognostic capabilities and best practices for manufacturing (2016, methodology): “2010), and data-driven prognostics (Schwabacher 2005).”
On condition based maintenance policy (2015, background): “It is also known as the data mining approach or the machine learning approach, which uses historical data to automatically learn a model of system behavior [30].”
Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction (2014, unknown): “Such methods employ pattern recognition and machine learning techniques to characterize the desired critical components’ degradation behavior (Schwabacher 2005).”
Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life (2011, methodology): “Many data-driven approaches [14,16] use the so-called holdout method, which divides the original run-to-failure data set into two mutually exclusive subsets called a training set and a testing set, or holdout set.”