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Prediction of remaining useful life of multi-stage aero-engine based on clustering and LSTM fusion

机译：基于聚类和LSTM融合的多级航空发动机剩余使用寿命的预测

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摘要

Accurately predicting the Remaining Useful Life (RUL) of an aero-engine is of great significance for airlines to make maintenance plans reasonably and reduce maintenance costs effectively. Traditional single-parameter and single-stage models achieve low prediction accuracy. In order to improve the prediction accuracy of the RUL of the aero-engine, a novel aero-engine RUL prediction model named Improved multi-stage Long Short Term Memory network with Clustering (ILSTMC) is proposed. Based on this model, we research a corresponding multi-stage RUL prediction algorithm, which integrates the advantages of clustering analysis and LSTM model. The National Aeronautics and Space Administration (NASA) dataset is adopted for verification. The experimental results show that the method provided in this paper reduces the prediction error of the aero-engine RUL effectively. In the cases of multi-stage prediction, the prediction error of ILSTMC is the smallest compared with LSTM, Recurrent Neural Networks (RNN) and Linear Programming (LP) methods. In the multi-stage prediction of RUL, it is evaluated adopting Root Mean Squared Error (RMSE) and prediction error. The RMSE of the last stage is reduced by 0.85% compared to LSTM, the RMSE of each stage is reduced by 1.87% compared to LSTM on average; the accuracy of life time cycle is better than LSTM by 0.59%, and the average accuracy of life time cycle at each stage is improved by 1.84% compared to LSTM. The results reveal that the proposed ILSTMC model effectively improves the prediction accuracy of RUL.
机译：准确地预测航空发动机的剩余使用寿命（RUL）对于航空公司来说具有重要意义，以合理地制造维护计划，有效降低维护成本。传统的单个参数和单级模型实现了低预测精度。为了提高航空发动机RUL的预测准确性，提出了一种名为改进的多级长短期存储网络的新型航空发动机RUL预测模型，其具有聚类（ILSTMC）。基于该模型，我们研究了一种相应的多阶段RUL预测算法，它集成了聚类分析和LSTM模型的优势。通过国家航空航天局（NASA）数据集进行核查。实验结果表明，本文提供的方法有效地降低了航空发动机RUL的预测误差。在多阶段预测的情况下，与LSTM，经常性神经网络（RNN）和线性编程（LP）方法相比，ILSTMC的预测误差是最小的。在RUL的多级预测中，评估采用根均方误差（RMSE）和预测误差。与LSTM相比，最后阶段的RMSE减少了0.85％，与平均LSTM相比，每个阶段的RMSE减少1.87％;生命周期的准确性优于LSTM，达到0.59％，并且与LSTM相比，每个阶段的生命时间循环的平均精度提高1.84％。结果表明，所提出的ILSTMC模型有效提高了RUL的预测准确性。

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  • 来源
    《Reliability Engineering & System Safety》 |2021年第10期|107807.1-107807.17|共17页
  • 作者

    Liu Junqiang; Lei Fan; Pan Chunlu; Hu Dongbin; Zuo Hongfu;

  • 作者单位

    Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing 210016 Peoples R China;

    Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing 210016 Peoples R China;

    Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing 210016 Peoples R China;

    Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing 210016 Peoples R China;

    Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing 210016 Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Multi-stage prediction; RUL; Clustering; Long short term memory network; RMSE;

    机译：多级预测;rul;聚类;长期内存网络;RMSE;

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