人工神经网络
预言
停工期
计算机科学
自编码
特征提取
人工智能
方位(导航)
平滑的
状态监测
机器学习
特征选择
工程类
数据挖掘
可靠性工程
电气工程
计算机视觉
作者
Min Xia,Teng Li,Tongxin Shu,Jiafu Wan,Clarence W. de Silva,Zhongren Wang
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2019-06-01
卷期号:15 (6): 3703-3711
被引量:190
标识
DOI:10.1109/tii.2018.2868687
摘要
The degradation of bearings plays a key role in the failures of industrial machinery. Prognosis of bearings is critical in adopting an optimal maintenance strategy to reduce the overall cost and to avoid unwanted downtime or even casualties by estimating the remaining useful life (RUL) of the bearings. Traditional data-driven approaches of RUL prediction rely heavily on manual feature extraction and selection using human expertise. This paper presents an innovative two-stage automated approach to estimate the RUL of bearings using deep neural networks (DNNs). A denoising autoencoder-based DNN is used to classify the acquired signals of the monitored bearings into different degradation stages. Representative features are extracted directly from the raw signal by training the DNN. Then, regression models based on shallow neural networks are constructed for each health stage. The final RUL result is obtained by smoothing the regression results from different models. The proposed approach has achieved satisfactory prediction performance for a real bearing degradation dataset with different working conditions.
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