Predicting Remaining Useful Life with Uncertainty Using Recurrent Neural Process

Gao, Guozhen; Que, Zijun; Xu, Zhengguo

doi:10.1109/qrs-c51114.2020.00057

Cited by 5 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Li et al [36] proposed a novel Bayesian Deep Learning (BDL)based framework to capture the combined effects of aleatoric uncertainty and epistemic uncertainty in RUL forecasting and adopted a sequential Bayesian boosting algorithm to unify the state transition and observation information through a single BDL model; a good RUL probability distribution prediction effect is finally achieved. Gao et al [37] obtained a series of RUL prediction values through RNN, assuming that RUL satisfies the Gaussian distribution, and used a Multilayer Perceptron (MLP) to obtain the probability of different RUL. Li et al [38] adopted a Just-in-time Learning (JITL) scheme to deal with the randomness of fault evolution and the diversity of degradation patterns.…”

Section: Literature Reviewmentioning

confidence: 99%

Data-Driven Predictive Maintenance Policy Based on Dynamic Probability Distribution Prediction of Remaining Useful Life

Xie,

Xue,

Zhang

et al. 2023

Machines

View full text Add to dashboard Cite

As the reliability, availability, maintainability, and safety of industrial equipment have become crucial in the context of intelligent manufacturing, there are increasing expectations and requirements for maintenance policies. Compared with traditional methods, data-driven Predictive Maintenance (PdM), a superior approach to equipment and system maintenance, has been paid considerable attention by scholars in this field due to its high applicability and accuracy with a highly reliable quantization basis provided by big data. However, current data-driven methods typically provide only point estimates of the state rather than quantification of uncertainty, impeding effective maintenance decision-making. In addition, few studies have conducted further research on maintenance decision-making based on state predictions to achieve the full functionality of PdM. A PdM policy is proposed in this work to obtain the continuous probability distribution of system states dynamically and make maintenance decisions. The policy utilizes the Long Short-Term Memory (LSTM) network and Kernel Density Estimation with a Single Globally-optimized Bandwidth (KDE-SGB) method to dynamic predicting of the continuous probability distribution of the Remaining Useful Life (RUL). A comprehensive optimization target is introduced to establish the maintenance decision-making approach acquiring recommended maintenance time. Finally, the proposed policy is validated through a bearing case study, indicating that it allows for obtaining the continuous probability distribution of RUL centralized over a range of ±10 sampling cycles. In comparison to the other two policies, it could reduce the maintenance costs by 24.49~70.02%, raise the availability by 0.46~1.90%, heighten the reliability by 0.00~27.50%, and promote more stable performance with various maintenance cost and duration. The policy has offered a new approach without priori hypotheses for RUL prediction and its uncertainty quantification and provided a reference for constructing a complete PdM policy integrating RUL prediction with maintenance decision-making.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Data-Driven Predictive Maintenance Policy Based on Dynamic Probability Distribution Prediction of Remaining Useful Life

Xie,

Xue,

Zhang

et al. 2023

Machines

View full text Add to dashboard Cite

show abstract

“…Therefore, several attempts have been conducted in the literature to predict the RUL of a turbofan engine. However, these attempts still suffer from the disadvantages of high computational power [13], uncertainty prediction [21,22], and further architecture optimization is required [23] in order to provide high prediction accuracy because even a little uncertainty in prognostics prediction can result in huge losses [24]. Thus, there is a need for accurately predicting the RUL in practical aerospace applications [13] due to the presence of various uncertainties that affect prognostic calculations that, in turn, render turbofan engine RUL predictions uncertain [25].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, there is a need for accurately predicting the RUL in practical aerospace applications [13] due to the presence of various uncertainties that affect prognostic calculations that, in turn, render turbofan engine RUL predictions uncertain [25]. Previous investigations [1,6,13,14,21,22,24] do not attempt to quantify the inherent uncertainty in their predictions. Hence, the research gaps identified by [25] still remain open in the context of RUL prediction in prognostics.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, the research gaps identified by [25] still remain open in the context of RUL prediction in prognostics. While methods based on deep learning can achieve promising point prediction performance, they struggle to quantify the uncertainty associated with RUL prediction [24]. To fill these gaps, we have proposed a new data-driven approach that aims to accurately predict RUL and overcome the uncertainty inherent in DNNs predictions in the literature by incorporating the sliding time window technique for sample preparation and long short-term memory (LSTM) network with an attention mechanism to map the relationship between features and the RUL effectively.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data-Driven Deep Learning-Based Attention Mechanism for Remaining Useful Life Prediction: Case Study Application to Turbofan Engine Analysis

et al. 2021

View full text Add to dashboard Cite

Accurately predicting the remaining useful life (RUL) of the turbofan engine is of great significance for improving the reliability and safety of the engine system. Due to the high dimension and complex features of sensor data in RUL prediction, this paper proposes four data-driven prognostic models based on deep neural networks (DNNs) with an attention mechanism. To improve DNN feature extraction, data are prepared using a sliding time window technique. The raw data collected after normalizing is simply fed into the suggested network, requiring no prior knowledge of prognostics or signal processing and simplifying the proposed method's applicability. In order to verify the RUL prediction ability of the proposed DNN techniques, the C-MAPSS benchmark dataset of the turbofan engine system is validated. The experimental results showed that the developed long short-term memory (LSTM) model with attention mechanism achieved accurate RUL prediction in both scenarios with a high degree of robustness and generalization ability. Furthermore, the proposed model performance outperforms several state-of-the-art prognosis methods, where the LSTM-based model with attention mechanism achieved an RMSE of 12.87 and 11.23 for FD002 and FD003 subset of data, respectively.

show abstract

Degradation modeling and remaining useful life prediction for a multi-component system with stochastic dependence

Niu

Zeng

Shi

et al. 2023

Computers & Industrial Engineering

View full text Add to dashboard Cite

Predicting Remaining Useful Life with Uncertainty Using Recurrent Neural Process

Cited by 5 publications

References 16 publications

Data-Driven Predictive Maintenance Policy Based on Dynamic Probability Distribution Prediction of Remaining Useful Life

Data-Driven Predictive Maintenance Policy Based on Dynamic Probability Distribution Prediction of Remaining Useful Life

Data-Driven Deep Learning-Based Attention Mechanism for Remaining Useful Life Prediction: Case Study Application to Turbofan Engine Analysis

Degradation modeling and remaining useful life prediction for a multi-component system with stochastic dependence

Contact Info

Product

Resources

About