- Title
- Data-driven prognosis method using hybrid deep recurrent neural network
- Creator
- Xia, Min; Zheng, Xi; Imran, Muhammad; Shoaib, Muhammad
- Date
- 2020
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/186171
- Identifier
- vital:16837
- Identifier
-
https://doi.org/10.1016/j.asoc.2020.106351
- Identifier
- ISBN:1568-4946 (ISSN)
- Abstract
- Prognostics and health management (PHM) has attracted increasing attention in modern manufacturing systems to achieve accurate predictive maintenance that reduces production downtime and enhances system safety. Remaining useful life (RUL) prediction plays a crucial role in PHM by providing direct evidence for a cost-effective maintenance decision. With the advances in sensing and communication technologies, data-driven approaches have achieved remarkable progress in machine prognostics. This paper develops a novel data-driven approach to precisely estimate the remaining useful life of machines using a hybrid deep recurrent neural network (RNN). The long short-term memory (LSTM) layers and classical neural networks are combined in the deep structure to capture the temporal information from the sequential data. The sequential sensory data from multiple sensors data can be fused and directly used as input of the model. The extraction of handcrafted features that relies heavily on prior knowledge and domain expertise as required by traditional approaches is avoided. The dropout technique and decaying learning rate are adopted in the training process of the hybrid deep RNN structure to increase the learning efficiency. A comprehensive experimental study on a widely used prognosis dataset is carried out to show the outstanding effectiveness and superior performance of the proposed approach in RUL prediction. © 2020 Elsevier B.V.
- Publisher
- Elsevier Ltd
- Relation
- Applied Soft Computing Journal Vol. 93, no. (2020), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2020 Elsevier B.V.
- Rights
- Open Access
- Subject
- 4602 Artificial intelligence; 4903 Numerical and computational mathematics; Long short-term memory; Prognostics; Recurrent neural network; Remaining useful life prediction
- Full Text
- Reviewed
- Funder
- This work is partially supported by the Taicang Innovation Leading Project ( TC2018DYDS21 ) and the Deanship of Scientific Research at King Saud University through the research group project number RG-1439-036
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