Remaining useful life prediction of planet bearings based on conditional deep recurrent generative adversarial network and action discovery

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.

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Sequence Adaptation Adversarial Network for Remaining Useful Life Prediction Using Small Data Set

2020 IEEE 18th International Conference on Industrial Informatics (INDIN) ◽

10.1109/indin45582.2020.9442160 ◽

2020 ◽

Author(s):

Haixin Lv ◽

Jinglong Chen ◽

Tongyang Pan

Keyword(s):

Life Prediction ◽

Remaining Useful Life ◽

Small Data ◽

Data Set ◽

Adversarial Network ◽

Small Data Set ◽

Useful Life

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Time-Series Regeneration with Convolutional Recurrent Generative Adversarial Network for Remaining Useful Life Estimation

IEEE Transactions on Industrial Informatics ◽

10.1109/tii.2020.3046036 ◽

2020 ◽

pp. 1-1

Author(s):

Xuewen Zhang ◽

Yan Qin ◽

Chau Yuen ◽

Lahiru Jayasinghe ◽

Xiang Liu

Keyword(s):

Time Series ◽

Remaining Useful Life ◽

Generative Adversarial Network ◽

Life Estimation ◽

Adversarial Network ◽

Useful Life

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Generative Adversarial Network-based Missing Data Handling and Remaining Useful Life Estimation for Smart Train Control and Monitoring Systems

Journal of Advanced Transportation ◽

10.1155/2020/8861942 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Hyunsoo Lee ◽

Seok-Youn Han ◽

Kee-Jun Park

Keyword(s):

Missing Values ◽

Remaining Useful Life ◽

Current Status ◽

Generative Adversarial Network ◽

Train Control ◽

Adversarial Network ◽

Maintenance Systems ◽

Useful Life ◽

Control And Monitoring System ◽

Railway Maintenance

As railway is considered one of the most significant transports, sudden malfunction of train components or delayed maintenance may considerably disrupt societal activities. To prevent this issue, various railway maintenance frameworks, from “periodic time-based and distance-based traditional maintenance frameworks” to “monitoring/conditional-based maintenance systems,” have been proposed and developed. However, these maintenance frameworks depend on the current status and situations of trains and cars. To overcome these issues, several predictive frameworks have been proposed. This study proposes a new and effective remaining useful life (RUL) estimation framework using big data from a train control and monitoring system (TCMS). TCMS data is classified into two types: operation data and alarm data. Alarm or RUL information is extracted from the alarm data. Subsequently, a deep learning model achieves the mapping relationship between operation data and the extracted RUL. However, a number of TCMS data have missing values due to malfunction of embedded sensors and/or low life of monitoring modules. This issue is addressed in the proposed generative adversarial network (GAN) framework. Both deep neural network (DNN) models for a generator and a predictor estimate missing values and predict train fault, simultaneously. To prove the effectiveness of the proposed GAN-based predictive maintenance framework, TCMS data-based case studies and comparisons with other methods were carried out.

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Remaining useful life prediction with insufficient degradation data based on deep learning approach

Eksploatacja i Niezawodnosc - Maintenance and Reliability ◽

10.17531/ein.2021.4.17 ◽

2021 ◽

Vol 23 (4) ◽

pp. 745-756

Author(s):

Yi Lyu ◽

Yijie Jiang ◽

Qichen Zhang ◽

Ci Chen

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Time Window ◽

Synthetic Data ◽

Remaining Useful Life ◽

Generative Adversarial Network ◽

Data Set ◽

Adversarial Network ◽

Degradation Data ◽

Useful Life

Remaining useful life (RUL) prediction plays a crucial role in decision-making in conditionbased maintenance for preventing catastrophic field failure. For degradation-failed products, the data of performance deterioration process are the key for lifetime estimation. Deep learning has been proved to have excellent performance in RUL prediction given that the degradation data are sufficiently large. However, in some applications, the degradation data are insufficient, under which how to improve the prediction accuracy is yet a challenging problem. To tackle such a challenge, we propose a novel deep learning-based RUL prediction framework by amplifying the degradation dataset. Specifically, we leverage the cycle-consistent generative adversarial network to generate the synthetic data, based on which the original degradation dataset is amplified so that the data characteristics hidden in the sample space could be captured. Moreover, the sliding time window strategy and deep bidirectional long short-term memory network are employed to complete the RUL prediction framework. We show the effectiveness of the proposed method by running it on the turbine engine data set from the National Aeronautics and Space Administration. The comparative experiments show that our method outperforms a case without the use of the synthetically generated data.

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