Remaining Useful Life Estimation of Aircraft Engines Using Differentiable Architecture Search

Prognostics and health management (PHM) applications can prevent engines from potential serious accidents by predicting the remaining useful life (RUL). Recently, data-driven methods have been widely used to solve RUL problems. The network architecture has a crucial impact on the experiential performance. However, most of the network architectures are designed manually based on human experience with a large cost of time. To address these challenges, we propose a neural architecture search (NAS) method based on gradient descent. In this study, we construct the search space with a directed acyclic graph (DAG), where a subgraph represents a network architecture. By using softmax relaxation, the search space becomes continuous and differentiable, then the gradient descent can be used for optimization. Moreover, a partial channel connection method is introduced to accelerate the searching efficiency. The experiment is conducted on C-MAPSS dataset. In the data processing step, a fault detection method is proposed based on the k-means algorithm, which drops large valueless data and promotes the estimation performance. The experimental result shows that our method achieves superior performance with the highest estimation accuracy compared with other popular studies.

Automated summary

This study proposes a neural architecture search method based on gradient descent to predict the remaining useful life (RUL) of engines and prevent potential serious accidents. Experimental results show that the proposed method achieves superior performance in estimation accuracy.