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Open AccessArticle

A Novel Deep Learning Approach for Machinery Prognostics Based on Time Windows

by Hanbo Yang 1, Fei Zhao 1,2, Gedong Jiang 1,2,*, Zheng Sun 1 and Xuesong Mei 1,2
1
State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2
Shanxi Key Laboratory of Intelligent Robots, Xi’an Jiaotong University, Xi’an 710049, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(22), 4813; https://doi.org/10.3390/app9224813 (registering DOI)
Received: 10 October 2019 / Revised: 1 November 2019 / Accepted: 7 November 2019 / Published: 11 November 2019
(This article belongs to the Section Mechanical Engineering)
Remaining useful life (RUL) prediction is a challenging research task in prognostics and receives extensive attention from academia to industry. This paper proposes a novel deep convolutional neural network (CNN) for RUL prediction. Unlike health indicator-based methods which require the long-term tracking of sensor data from the initial stage, the proposed network aims to utilize data from consecutive time samples at any time interval for RUL prediction. Additionally, a new kernel module for prognostics is designed where the kernels are selected automatically, which can further enhance the feature extraction ability of the network. The effectiveness of the proposed network is validated using the C-MAPSS dataset for aircraft engines provided by NASA. Compared with the state-of-the-art results on the same dataset, the prediction results demonstrate the superiority of the proposed network. View Full-Text
Keywords: remaining useful life; convolutional neural network; kernel remaining useful life; convolutional neural network; kernel
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Yang, H.; Zhao, F.; Jiang, G.; Sun, Z.; Mei, X. A Novel Deep Learning Approach for Machinery Prognostics Based on Time Windows. Appl. Sci. 2019, 9, 4813.

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