The goal of data-driven methods is to remove dependence on classical models of structured expert judgment and draw insights to causal relationships directly from the data. This paper investigates the potential of using data-driven methods, namely uni-variate multiple linear regression, k-nearest neighbors, feed-forward neural networks, random forests and linear support vector regression to predict the end of life
(EOL) and remaining useful life (RUL) of engineering systems. The algorithms are demonstrated on a real-world largescale dataset consisting of a multidimensional time series of health monitoring indicators collected from a set of commercial aircraft gas turbine engines. A stratified version of
10-fold cross-validation is used to compare the prognostics performance of the five prognostics models. An experiencebased Weibull model is chosen as the baseline method. Models are evaluated according to established metrics in the field including median absolute error, median absolute deviation and relative accuracy. The prediction results indicate that support vector regression and random forests are the most accurate
models. Neural networks and k-nearest neighbors also show improved forecast skill compared to the baseline model while beating the more traditional technique of linear regression. In regards to error spread, results are not as expressive even though all the selected data-driven methods provide good results, outperforming the baseline.
How to Cite
Data-driven Methods, PHM industrial applications, Life Usage Modelling
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