Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (5): 1527-1532.DOI: 10.11772/j.issn.1001-9081.2020071125

Special Issue: 前沿与综合应用

• Frontier & interdisciplinary applications • Previous Articles     Next Articles

Remaining useful life prediction of DA40 aircraft carbon brake pads based on bidirectional long short-term memory network

XU Meng, WANG Yakun   

  1. College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
  • Received:2020-07-29 Revised:2020-10-09 Online:2021-05-10 Published:2020-12-23
  • Supported by:
    This work is partially supported by the Aviation Science Foundation (2019ZD067007).


徐萌, 王亚锟   

  1. 中国民航大学 电子信息与自动化学院, 天津 300300
  • 通讯作者: 王亚锟
  • 作者简介:徐萌(1968-),女,江苏南京人,副教授,硕士,主要研究方向:航空地面测试设备与故障诊断、航空器安全评估、飞机部件无损检测方法;王亚锟(1996-),男,河北唐山人,硕士研究生,主要研究方向:神经网络、机器学习、机载设备寿命预测。
  • 基金资助:

Abstract: Aircraft brake pads play a very important role in the process of aircraft braking. It is of great significance to accurately predict the Remaining Useful Life (RUL) of aircraft brake pads for reducing braking faults and saving human and material resources. Aiming at the non-stationary and nonlinear characteristics of the aircraft brake pads wear sequence, a model for predicting the RUL of the aircraft brake pads based on Bidirectional Long Short-Term Memory (BiLSTM) network was proposed, namely VMD-BiLSTM model. Firstly, the method of Variational Mode Decomposition (VMD) was used to decompose the original wear sequence into several sub-sequences with different frequencies and bandwidths to reduce the non-stationarity of the sequence. Then, the BiLSTM neural network prediction models were constructed for the decomposed subsequences. Finally, the prediction values of the sub-sequences were superimposed to obtain the final prediction result of brake pads wear value, so as to realize the life prediction of the brake pads. The simulation results show that the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE) of VMD-BiLSTM model are 0.466 and 0.898% respectively, both of which are better than those of the comparison models, verifying the superiority of VMD-BiLSTM model.

Key words: Bidirectional Long Short-Term (BiLSTM) network, Variational Mode Decomposition (VMD), carbon brake pad, Remaining Useful Life (RUL), neural network

摘要: 飞机刹车片在飞机制动过程中起着十分重要的作用。对刹车片进行准确的剩余使用寿命(RUL)预测对于减少制动故障以及节省人力物力资源具有重要意义。针对飞机刹车片磨损序列的非平稳和非线性等特点,提出了一种基于双向长短期记忆(BiLSTM)网络的飞机刹车片RUL预测模型——VMD-BiLSTM模型。首先,利用变分模态分解(VMD)方法将原始磨损序列分解成多个具有不同频率和带宽的子序列,从而降低序列的非平稳性;然后,对分解后的各子序列分别构造BiLSTM神经网络预测模型;最后,将每个子序列的预测值叠加来得到刹车片磨损值的最终预测结果,从而实现刹车片的寿命预测。仿真结果表明,VMD-BiLSTM模型的均方根误差(RMSE)为0.466,平均绝对百分比误差(MAPE)为0.898%,均优于对比模型,验证了VMD-BiLSTM模型的优越性。

关键词: 双向长短期记忆网络, 变分模态分解, 碳刹车片, 剩余使用寿命, 神经网络

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