.png){kind=icon}
返回
Remaining useful life estimation in prognostics using deep convolution neural networks
.png){kind=icon}
OA
AI
DOI：10.1016/j.ress.2017.11.021.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Traditionally, system prognostics and health management (PHM) depends on sufficient prior knowledge of critical components degradation process in order to predict the remaining useful life (RUL). However, the accurate physical or expert models are not available in most cases. This paper proposes a new data-driven approach for prognostics using deep convolution neural networks (DCNN). Time window approach is employed for sample preparation in order for better feature extraction by DCNN. Raw collected data with normalization are directly used as inputs to the proposed network, and no prior expertise on prognostics and signal processing is required, that facilitates the application of the proposed method. In order to show the effectiveness of the proposed approach, experiments on the popular C-MAPSS dataset for aero-engine unit prognostics are carried out. High prognostic accuracy on the RUL estimation is achieved. The superiority of the proposed method is demonstrated by comparisons with other popular approaches and the state-of-the-art results on the same dataset. The results of this study suggest that the proposed data-driven prognostic method offers a new and promising approach. (C) 2017 Elsevier Ltd. All rights reserved.
Keyword:
Prognostics and health management
Remaining useful life
Deep learning
Convolution neural network
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
R
IF:
11
论文数:
9.0K
被引数:
4.2W
机构
引用论文
Predicting component reliability and level of degradation with complex-valued neural networks用复值神经网络预测元件的可靠性和退化程度OAAI
OAAI分享收藏A sparse auto-encoder-based deep neural network approach for induction motor faults classification.png)
MEASUREMENT
MEASUREMENTIF5.6
PREAI
分享收藏Prognostics and health management design for rotary machinery systems-Reviews, methodology and applications旋转机械系统的预测和健康管理设计-评论，方法和应用
PREAI
分享收藏PHM-Oriented Integrated Fusion Prognostics for Aircraft Engines Based on Sensor DataIEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
分享收藏
查看更多内容