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

Sun, Chuang ; Ma, Meng ; Zhao, Zhibin ; Tian, Shaohua ; Yan, Ruqiang ; Chen, Xuefeng

IEEE transactions on industrial informatics, 2019-04, Vol.15 (4), p.2416-2425

Piscataway: IEEE

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Título:
Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing
Autor: Sun, Chuang ; Ma, Meng ; Zhao, Zhibin ; Tian, Shaohua ; Yan, Ruqiang ; Chen, Xuefeng
Assuntos: 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
É parte de: IEEE transactions on industrial informatics, 2019-04, Vol.15 (4), p.2416-2425
Descrição: 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.
Editor: Piscataway: IEEE
Idioma: Inglês

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

Sun, Chuang ; Ma, Meng ; Zhao, Zhibin ; Tian, Shaohua ; Yan, Ruqiang ; Chen, Xuefeng

Piscataway: IEEE

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