Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing

Deep learning with ability to feature learning and nonlinear function approximation has shown its effectiveness for machine fault prediction. While, how to transfer a deep network trained by historical failure data for prediction of a new object is rarely researched. In this paper, a deep transfer l...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics Vol. 15; no. 4; pp. 2416 - 2425
Main Authors: Sun, Chuang, Ma, Meng, Zhao, Zhibin, Tian, Shaohua, Yan, Ruqiang, Chen, Xuefeng
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.04.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Cutting tools
Deep learning
deep transfer learning (DTL)
Failure
Fault diagnosis
Feature extraction
Hidden Markov models
Life prediction
Machine learning
Maintenance management
Monitoring
Predictive models
remaining useful life (RUL) prediction
sparse autoencoder (SAE)
Tool life
transfer learning
Useful life
Weight
ISSN:1551-3203, 1941-0050
Online Access:Get full text
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Summary:Deep learning with ability to feature learning and nonlinear function approximation has shown its effectiveness for machine fault prediction. While, how to transfer a deep network trained by historical failure data for prediction of a new object is rarely researched. In this paper, a deep transfer learning (DTL) network based on sparse autoencoder (SAE) is presented. In the DTL method, three transfer strategies, that is, weight transfer, transfer learning of hidden feature, and weight update, are used to transfer an SAE trained by historical failure data to a new object. By these strategies, prediction of the new object without supervised information for training is achieved. Moreover, the learned features by deep transfer network for the new object share joint and similar characteristic to that of historical failure data, which is beneficial to accurate prediction. Case study on remaining useful life (RUL) prediction of cutting tool is performed to validate effectiveness of the DTL method. An SAE network is first trained by run-to-failure data with RUL information of a cutting tool in an off-line process. The trained network is then transferred to a new tool under operation for on-line RUL prediction. The prediction result with high accuracy shows advantage of the DTL method for RUL prediction.
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2018.2881543