Long short-term memory for machine remaining life prediction

Zhang, Jianjing; Wang, Peng; Yan, Ruqiang; Gao, Robert X.

doi:10.1016/j.jmsy.2018.05.011

Cited by 332 publications

(128 citation statements)

References 26 publications

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“…To overcome this situation, we present in this section a new data-driven prognostic method that directly provides the probability of the system failure without prior knowledge of the failure mechanism. This method is based on the Long Short-Term Memory (LSTM) networks, one of Recurrent Neural Network (RNN) architectures, that has received increasing attention in recent prognostics studies [22][23][24][25][26][27]35]. One of the main advantages of the LSTM is the capacity of learning over Jong time sequences and retaining memory.…”

Section: New Dynamic Predictive Maintenance Frameworkmentioning

confidence: 99%

“…For recent studies, the article [21] developed an integrated hierarchical learning framework to perform both diagnostics and prognostics. In papers [22,23], the authors proposed to use the Long Short-Term Memory (LSTM) network, which is an architecture specialized in discovering the underlying time series patterns to predict the system RUL. In [24], a new deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM) was designed to address raw sensory data for RUL prediction.…”

Section: Introductionmentioning

confidence: 99%

“…In detail, we propose to use the LSTM network to estimate the probabilities that the system will fail in different time windows in the future. Although the LSTM network has been developed and improved in PHM literature, the previous studies [22][23][24][25][26][27] are based on a piece-wise linear (PWL) RUL target function, in which it is not trivial to define the RUL maximum value. Moreover, in these studies, the prognostics is treated as a regression problem.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A new dynamic predictive maintenance framework using deep learning for failure prognostics

Nguyen

Medjaher

2019

Reliability Engineering & System Safety

233

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Section: New Dynamic Predictive Maintenance Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A new dynamic predictive maintenance framework using deep learning for failure prognostics

Nguyen

Medjaher

2019

Reliability Engineering & System Safety

233

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show abstract

“…Ahmed used an end-to-end deep framework based on convolutional and LSTM to deal with RUL estimation problems [17]. Zhang apply the LSTM networks to set up an architecture that is specialized in discovering the underlying patterns embedded in time series to track the system degradation and predict the RUL [18].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Recurrent Neural Network-Based Residual Useful Life Prognostics of Aircraft Engines

Wu¹,

Hu²,

Cheng³

et al. 2019

Structural Durability &Amp; Health Monitoring

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Residual useful life (RUL) prediction is a key issue for improving efficiency of aircraft engines and reducing their maintenance cost. Owing to various failure mechanism and operating environment, the application of classical models in RUL prediction of aircraft engines is fairly difficult. In this study, a novel RUL prognostics method based on using ensemble recurrent neural network to process massive sensor data is proposed. First of all, sensor data obtained from the aircraft engines are preprocessed to eliminate singular values, reduce random fluctuation and preserve degradation trend of the raw sensor data. Secondly, three kinds of recurrent neural networks (RNN), including ordinary RNN, long shortterm memory (LSTM), and gated recurrent unit (GRU), are individually constructed. Thirdly, ensemble learning mechanism is designed to merge the above RNNs for producing a more accurate RUL prediction. The effectiveness of the proposed method is validated using two characteristically different turbofan engine datasets. Experimental results show a competitive performance of the proposed method in comparison with typical methods reported in literatures.

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“…With the development of sensor technology, data explosion has become a new problem of bearing remaining life prediction, which has made artificial intelligence technology develop rapidly in recent years. Zhang et al [22] constructed a long short-term memory (LSTM) network to predict the RUL of mechanical equipment and verified the advantages of LSTM in RUL prediction by using C-MAPSS datasets. Zhao et al[23] presented a recurrent neural network (RNN) means for RUL prediction based on trend characteristics, and the effect of the presented method outperformed the other latest methods.…”

mentioning

confidence: 98%

A Reliable Prognosis Approach for Degradation Evaluation of Rolling Bearing Using MCLSTM

Huang

et al. 2020

Sensors

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Prognostics and health management technology (PHM), a measure to ensure the reliability and safety of the operation of industrial machinery, has attracted attention and application adequately. However, how to use the monitored information to evaluate the degradation of rolling bearings is a significant issue for its predictive maintenance and autonomic logistics. This work presents a reliable health prognosis approach to estimate the health indicator (HI) and remaining useful life (RUL) of rolling bearings. Firstly, to accurately capture the degradation process, a novel health index (HI) is constructed based on correlation kurtosis for different iteration periods and a Gaussian process latency variable model (GPLVM). Then, a multiple convolutional long short-term memory (MCLSTM) network is proposed to predict HI values and RUL values. Finally, we perform experimental datasets of rolling bearings, demonstrating that the presented method surpasses other state-of-the-art prognosis approaches. The results also confirm the feasibility of the presented method in industrial machinery.

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Long short-term memory for machine remaining life prediction

Cited by 332 publications

References 26 publications

A new dynamic predictive maintenance framework using deep learning for failure prognostics

A new dynamic predictive maintenance framework using deep learning for failure prognostics

Ensemble Recurrent Neural Network-Based Residual Useful Life Prognostics of Aircraft Engines

A Reliable Prognosis Approach for Degradation Evaluation of Rolling Bearing Using MCLSTM

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