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Volume 8 Issue 2
Feb.  2021

IEEE/CAA Journal of Automatica Sinica

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Chuang Chen, Ningyun Lu, Bin Jiang and Cunsong Wang, "A Risk-Averse Remaining Useful Life Estimation for Predictive Maintenance," IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 412-422, Feb. 2021. doi: 10.1109/JAS.2021.1003835
Citation: Chuang Chen, Ningyun Lu, Bin Jiang and Cunsong Wang, "A Risk-Averse Remaining Useful Life Estimation for Predictive Maintenance," IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 412-422, Feb. 2021. doi: 10.1109/JAS.2021.1003835

A Risk-Averse Remaining Useful Life Estimation for Predictive Maintenance

doi: 10.1109/JAS.2021.1003835
Funds:  This work was support by Natural Science Foundation of China (61873122)
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  • Remaining useful life (RUL) prediction is an advanced technique for system maintenance scheduling. Most of existing RUL prediction methods are only interested in the precision of RUL estimation; the adverse impact of over-estimated RUL on maintenance scheduling is not of concern. In this work, an RUL estimation method with risk-averse adaptation is developed which can reduce the over-estimation rate while maintaining a reasonable under-estimation level. The proposed method includes a module of degradation feature selection to obtain crucial features which reflect system degradation trends. Then, the latent structure between the degradation features and the RUL labels is modeled by a support vector regression (SVR) model and a long short-term memory (LSTM) network, respectively. To enhance the prediction robustness and increase its marginal utility, the SVR model and the LSTM model are integrated to generate a hybrid model via three connection parameters. By designing a cost function with penalty mechanism, the three parameters are determined using a modified grey wolf optimization algorithm. In addition, a cost metric is proposed to measure the benefit of such a risk-averse predictive maintenance method. Verification is done using an aero-engine data set from NASA. The results show the feasibility and effectiveness of the proposed RUL estimation method and the predictive maintenance strategy.

     

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    Highlights

    • Degradation feature selection procedure helps to lessen calculative burden.
    • Hybrid model helps to enhance prediction robustness and increase marginal utility.
    • Evolutionary algorithm helps to determine hybrid model parameters.
    • Cost function with penalty mechanism allows alleviating prediction risk.
    • Cost metric allows measuring risk-averse predictive maintenance benefit.

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