Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme

Yu, Wennian; Kim, Il Yong; Mechefske, Chris K.

doi:10.1016/j.ymssp.2019.05.005

Cited by 283 publications

(106 citation statements)

References 26 publications

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“…Yu et al obtained one-dimensional HI values from sensor data via the bidirectional RNN-based autoencoder, which represents the degradation patterns of the units of the system. Then, they used the similarity-based curve matching technique to estimate the RUL [ 27 ]. Zhang et al combined the deep belief network (DBN) training technique with a multiobjective evolutionary algorithm to evolve multiple DBNs with varying accuracies and diversities.…”

Section: Related Work In the C-mapss Datasetmentioning

confidence: 99%

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

Hou

Zhou

et al. 2020

Computational Intelligence and Neuroscience

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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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Section: Related Work In the C-mapss Datasetmentioning

confidence: 99%

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

Hou

Zhou

et al. 2020

Computational Intelligence and Neuroscience

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“…The turbine engine data (C-MAPSS) that is published by NASA is applied to demonstrate the effectiveness of the proposed method for series systems, which contains four subsets (named FD001 ∼ FD004). Details of these subsets can be accessed in [26], and the subset involving a single failure mode and a single operating condition (FD001) is adopted here. The proposed joint-RUL-prediction method with multiple degradation indicators (OTKS-PF-Joint) is used to predict the RUL of the engines, and the prediction result is compared with the RUL result that obtained with a single degradation indicator (OTKS-PF-Single).…”

Section: Experimental Verificationmentioning

confidence: 99%

Remaining Useful Life Prediction for Complex Systems With Multiple Indicators Based on Particle Filter and Parameter Correlation

et al. 2020

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In practical applications, the failure of large-scale complex equipment is often caused by the simultaneous degradation of multiple components. It is necessary to predict the remaining useful life (RUL) of the equipment with multiple degradation indicators. This paper proposes a new joint-RUL-prediction method in the presence of multiple degradation indicators based on parameter correlation. The stochastic process model is established for each degradation indicator, and the model parameters are estimated by kernel smoothing particle filter (KS-PF) and maximum likelihood estimation (MLE). Meanwhile, to facilitate the dependencies between multiple degradation indicators, correlations of the degradation model parameter between multiple degradation indicators are established in KS-PF. In addition, optimal tuning (OT) is introduced to choose the best kernel parameter. A case study on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset is applied to verify the proposed method, the experiment shows that the proposed joint-RUL-prediction method based on parameter correlation possesses a superior prediction performance compared with that by using a single degradation indicator.

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“…A data-driven prediction method based on Elman neural network is proposed by Yang et al [25]. A method that uses deep learning tools and curve matching technology is proposed to estimate the robustness of the system [26]. A framework for estimating the RUL of mechanical systems is proposed, which is composed of the multi-layer perceptron and multilayer perceptron and evolutionary algorithm for optimizing parameters [27].…”

Section: New Faultmentioning

confidence: 99%

An Improved PF Remaining Useful Life Prediction Method Based on Quantum Genetics and LSTM

Yang

Sun

2019

IEEE Access

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Remaining useful life (RUL) is the premise and basis of the equipment health management plan. As accurate as possible life prediction is of great significance to reliability and economy of equipment maintenance. In this paper, a data-driven improved particle filter (PF) RUL prediction method is proposed. A health indicator extraction method based on multi-feature fusion is introduced for the RUL prediction, which can visually show the degradation trend of the healthy state of the equipment. The degradation model and observation model of equipment health indicators are established, and the PF algorithm is used to track parameters of the model. A quantum genetic algorithm is employed to improve the problem of particle degradation in PF. On the basis of filter tracking, long short term memory (LSTM) network is used to predict the trend of model coefficients, which further improves the accuracy of RUL prediction. The experiment using the C-MAPSS data set shows the proposed method has a better prediction accuracy than other methods. INDEX TERMS Remaining useful life, particle filter, quantum genetic algorithm, long short term memory.

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Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme

Cited by 283 publications

References 26 publications

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

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

Remaining Useful Life Prediction for Complex Systems With Multiple Indicators Based on Particle Filter and Parameter Correlation

An Improved PF Remaining Useful Life Prediction Method Based on Quantum Genetics and LSTM

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