Traditionally, prognostics approaches to predictive maintenance have focused on estimating the remaining useful life of the equipment. However, from an industrial point of view, the goal is often not to predict the residual life but to determine the need for a maintenance action at a given time window. This approach allows us to frame the data-driven prognostics problem as a binary classification task rather than a regression one. To address this problem, we propose in this paper to explore the relative strengths and limitations of a set of classifier approaches such as random forests, support vector machines, nearest neighbors, and deep learning techniques. We evaluate the models using metrics such as sensitivity, specificity, accuracy, receiver operating characteristic curve, and F-score. This work's novelty lies in adopting a modeling approach with a natural probabilistic interpretation of the prognostics exercise. The comparison of an extensive range of classifier models is performed on two real-world datasets from the aeronautics sector. Results indicate that deep learning classifier methods are well suited for this kind of prognostics and can outperform by a significant margin the traditional classification techniques. Importantly, the proposed modeling approach aims to generate an alternative prognostics representation that goes in line with the expectations of aeronautical engineers.
|Number of pages||10|
|Journal||Measurement: Journal of the International Measurement Confederation|
|Publication status||Published - 2021|
- Case study
- Deep learning
- Predictive maintenance
- Recurrent neural networks