首页|Integrated framework for estimating remaining useful lifetime through a deep neural network
Integrated framework for estimating remaining useful lifetime through a deep neural network
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NSTL
Elsevier
This paper proposes an integrated framework for a deep neural network to estimate the remaining useful life (RUL) to ensure the reliability and safety of complex mechanical systems and enable proactive maintenance for intelligent operation. This data-driven method can predict complex and highly nonlinear degradation characteristics that are difficult to predict using physics-based prognostics and health management. In particular, this study focused on feature preprocessing and hyperparameter optimization, whereas previous studies had focused on the neural network architecture to improve prediction accuracy and robustness. The proposed integrated framework comprises four phases: feature preprocessing, feature reasoning using a deep neural network, hyperparameter optimization using a genetic algorithm, and RUL estimation. In the first phase, sensor measurements sensitive to degradation are selected and separated into primary and dynamic degradation trends. In addition, step differential values are extracted to account for multiple operational modes using an unsupervised clustering method. In the second phase, feature reasoning is performed using a deep neural network to characterize hidden complex and highly nonlinear degradation features. The health indicators manipulated in the first phase are trained using the proposed deep neural network. In the third phase, a genetic algorithm is introduced to optimize the hyperparameters used in feature preprocessing and reasoning. The final phase estimates the RUL using the proposed deep neural network with optimized hyperparameters. The proposed method was validated on the C-MAPSS dataset. The results show that the proposed integrated framework outperformed other state-of-the-art machine learning and deep learning methods under different operational conditions, suggesting that efficient feature preprocessing and hyperparameter optimization significantly improve the prediction accuracy and robustness of RUL for data-driven prognostics and health management. (C) 2022 Elsevier B.V. All rights reserved.
NSTL
Elsevier
