Remaining Useful Life Prognosis Based on Ensemble Long Short-Term Memory Neural Network

Yiwei Cheng, Jun Wu, Haiping Zhu, Siu Wing Or, Xinyu Shao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

18 Citations (Scopus)


Remaining useful life (RUL) prognosis is of great significance to improve the reliability, availability, and maintenance cost of an industrial equipment. Traditional machine learning method is not fit for dealing with time series signals and has low generalization and stability in prognostic. In this article, a novel ensemble long short-term memory neural network (ELSTMNN) model for RUL prediction is proposed to enhance the RUL prognosis accuracy and improve the adaptive and generalization abilities under different prognostic scenarios. The ELSTMNN contains a series of long short-term memory neural networks (LSTMNNs), each of which is trained on a unique set of historical data. A novel ensemble method is first proposed using Bayesian inference algorithm to integrate multiple predictions of the LSTMNNs for the optimal RUL estimation. The effectiveness of the ELSTMNN-based RUL prognosis method is validated using two characteristically different turbofan engine data sets. The experimental results show a competitive performance of the ELSTMNN in comparison with other prognostic methods.

Original languageEnglish
Article number9226143
JournalIEEE Transactions on Instrumentation and Measurement
Publication statusPublished - 15 Oct 2020


  • Bayesian inference algorithm (BIA)
  • ensemble learning (EL)
  • ensemble long short-term memory neural network (ELSTMNN)
  • remaining useful life (RUL) prognosis

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

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