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Remaining Useful Life Estimation Using Deep Convolutional Generative Adversarial Networks Based on an Autoencoder Scheme.
- Source :
-
Computational intelligence and neuroscience [Comput Intell Neurosci] 2020 Aug 01; Vol. 2020, pp. 9601389. Date of Electronic Publication: 2020 Aug 01 (Print Publication: 2020). - Publication Year :
- 2020
-
Abstract
- Accurate predictions of remaining useful life (RUL) of important components play a crucial role in system reliability, which is the basis of prognostics and health management (PHM). This paper proposed an integrated deep learning approach for RUL prediction of a turbofan engine by integrating an autoencoder (AE) with a deep convolutional generative adversarial network (DCGAN). In the pretraining stage, the reconstructed data of the AE not only participate in its error reconstruction but also take part in the DCGAN parameter training as the generated data of the DCGAN. Through double-error reconstructions, the capability of feature extraction is enhanced, and high-level abstract information is obtained. In the fine-tuning stage, a long short-term memory (LSTM) network is used to extract the sequential information from the features to predict the RUL. The effectiveness of the proposed scheme is verified on the NASA commercial modular aero-propulsion system simulation (C-MAPSS) dataset. The superiority of the proposed method is demonstrated via excellent prediction performance and comparisons with other existing state-of-the-art prognostics. The results of this study suggest that the proposed data-driven prognostic method offers a new and promising prediction approach and an efficient feature extraction scheme.<br />Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper.<br /> (Copyright © 2020 Guisheng Hou et al.)
- Subjects :
- Computer Simulation
Datasets as Topic
Humans
Prognosis
Deep Learning
Subjects
Details
- Language :
- English
- ISSN :
- 1687-5273
- Volume :
- 2020
- Database :
- MEDLINE
- Journal :
- Computational intelligence and neuroscience
- Publication Type :
- Academic Journal
- Accession number :
- 32802032
- Full Text :
- https://doi.org/10.1155/2020/9601389