Remaining Useful Life Estimation Using Deep Convolutional Generative Adversarial Networks Based on an Autoencoder Scheme

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 aut...

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Bibliographic Details
Published in:Computational intelligence and neuroscience 2020, Vol.2020 (2020), p.1-14
Main Authors: Yang, Lei, Zhou, Nan, Xu, Shuo, Hou, Guisheng, Fu, Quanhao
Format: Article
Language:English
Subjects:
Accuracy
Aircraft
Algorithms
Analysis
Cable television broadcasting industry
Commercial spacecraft
Component reliability
Datasets
Deep learning
Feature extraction
Generative adversarial networks
Information processing
Long short-term memory
Machine learning
Methods
Modular systems
Neural networks
Predictions
Propulsion systems
Sensors
System reliability
Turbofan engines
Useful life
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Summary: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.
ISSN:1687-5265
1687-5273
DOI:10.1155/2020/9601389