Time series multiple channel convolutional neural network with attention-based long short- term memory for predicting bearing remaining useful life

Jehn Ruey Jiang, Juei En Lee, Yi Ming Zeng

Research output: Contribution to journalArticlepeer-review

40 Scopus citations

Abstract

This paper proposes two deep learning methods for remaining useful life (RUL) prediction of bearings. The methods have the advantageous end-to-end property that they take raw data as input and generate the predicted RUL directly. They are TSMC-CNN, which stands for the time series multiple channel convolutional neural network, and TSMC-CNN-ALSTM, which stands for the TSMC-CNN integrated with the attention-based long short-term memory (ALSTM) network. The proposed methods divide a time series into multiple channels and take advantage of the convolutional neural network (CNN), the long short-term memory (LSTM) network, and the attention-based mechanism for boosting performance. The CNN performs well for extracting features from data with multiple channels; dividing a time series into multiple channels helps the CNN extract relationship among far-apart data points. The LSTM network is excellent for processing temporal data; the attention-based mechanism allows the LSTM network to focus on different features at different time steps for better prediction accuracy. PRONOSTIA bearing operation datasets are applied to the proposed methods for the purpose of performance evaluation and comparison. The comparison results show that the proposed methods outperform the others in terms of the mean absolute error (MAE) and the root mean squared error (RMSE) of RUL prediction.

Original languageEnglish
Article number166
JournalSensors (Switzerland)
Volume20
Issue number1
DOIs
StatePublished - 1 Jan 2020

Keywords

  • Bearing
  • Convolutional neural network
  • Deep learning
  • Long short-term memory
  • Remaining useful life
  • Time series

Fingerprint

Dive into the research topics of 'Time series multiple channel convolutional neural network with attention-based long short- term memory for predicting bearing remaining useful life'. Together they form a unique fingerprint.

Cite this