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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2019  >  19(5): 106-115
CHE Chang-chang, WANG Hua-wei, NI Xiao-mei, FU Qiang. Residual life prediction of aeroengine based on multi-scale permutation entropy and LSTM neural network[J]. Journal of Traffic and Transportation Engineering, 2019, 19(5): 106-115. doi: 10.19818/j.cnki.1671-1637.2019.05.011
Citation: CHE Chang-chang, WANG Hua-wei, NI Xiao-mei, FU Qiang. Residual life prediction of aeroengine based on multi-scale permutation entropy and LSTM neural network[J]. Journal of Traffic and Transportation Engineering, 2019, 19(5): 106-115. doi: 10.19818/j.cnki.1671-1637.2019.05.011
shu

Residual life prediction of aeroengine based on multi-scale permutation entropy and LSTM neural network

doi: 10.19818/j.cnki.1671-1637.2019.05.011

  • School of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu, China

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  • Author Bio:

    CHE Chang-chang(1994-), male, doctoral student, 821116775@qq.com

    WANG Hua-wei (1974-), female, professor, PhD, wang_hw66@163.com

  • Received Date: 2019-05-11
  • Publish Date: 2019-10-25
    Abstract
  • Aiming at the change point of aeroengine performance degradation failure and the time series prediction of multi-state parameters, the residual life prediction model based on the multi-scale permutation entropy(MPE) algorithm and long-short term memory(LSTM) neural network was constructed. The change points in time series were analyzed by the MPE algorithm, and the mutation points in the process of performance degradation were solved. The starting point of performance degradation with fault symptoms was obtained. The LSTM neural network model with multi-variables was constructed, and the corresponding residual life was obtained by introducing the multi-state parameter data into the model.The aeroengine multi-state parameters and residual life after the change point were taken as samples and substituted into the LSTM neural network model, the multi-step and multi-variable time series prediction was carried out.The final residual life prediction results were obtained by integrating the state parameter change point analysis method and time series prediction model of aeroengine. Research result shows that the MPE algorithm can monitor the changes of state parameters in time. When abnormal state parameters are found, the value of permutation entropy will jump, which is helpful to discover the fault symptoms in time. The LSTM neural network model selects the information of long time series data through the gated units, and the effective information can be fully reserved for the time series prediction. The multi-variable LSTM neural network can synchronously analyze the multi-state parameters, and directly correspond to the residual life, which improves the efficiency of the model. The combination of MPE algorithm and LSTM neural network model can take the multiple degradation modes of aeroengine into account, and the residual life prediction results of aeroengine are more in line with the actual degradation process. After an example analysis, the root mean square error of the proposed residual life prediction method is 5.3, which is 63%, 72% and 78% lower than that of LSTM neural network, back-propagation neural network and support vector machine, respectively.

     

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