Automatic multi-differential deep learning and its application to machine remaining useful life prediction

Xiang Sheng; Qin Yi; Liu FuqiangGryllias Konstantinos

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Automatic multi-differential deep learning and its application to machine remaining useful life prediction

机译：Automatic multi-differential deep learning and its application to machine remaining useful life prediction

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Different levels of characteristic information cannot be mined using various feature extraction modes in most neural networks, and thus, a novel method called the automatic multi-differential learning deep neural network (ADLDNN) is proposed in this work. First, a measurement-level division unit is designed for actively classifying multisource measurements into several levels. Then, a multibranch convolutional neural network (MBCNN), in which each branch can execute the corresponding feature extraction in accordance with the level of its input data, is constructed. Second, a multicellular bidirectional long short-term memory is proposed. A bidirectional trend-level division unit is used for actively classifying the output features of MBCNN into several levels of degradation trend along the forward and backward directions. Each cell unit implements the corresponding feature learning on the basis of the bidirectional trend level. Finally, the remaining useful life of a machine is predicted via a fully connected layer and the linear fitting of a regression layer. The effectiveness of the proposed method is validated on the widely used C-MAPSS dataset and an actual wind turbine gearbox bearing dataset. Comparative results show that the proposed ADLDNN is superior to state-of-the-art methods.

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《Reliability engineering & system safety》 |2022年第7期|108531.1-108531.12|共12页
作者
Xiang Sheng; Qin Yi; Liu FuqiangGryllias Konstantinos;
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作者单位

Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China|Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing 400044, Peoples R China;

Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China|Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing 400044, Peoples R China|Katholieke Univ Leuven, Dept Mech Engn, Celestijnenlaan 300,Box 2420, B-3001 Leuven, Belgium|Flande;

Katholieke Univ Leuven, Dept Mech Engn, Celestijnenlaan 300,Box 2420, B-3001 Leuven, Belgium|Flanders Make, Dynam Mech & Mechatron Syst, Lommel, Belgium;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
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关键词
RUL prediction; Multi-differential processing; Deep learning; C-MAPSS; Wind turbines;

Automatic multi-differential deep learning and its application to machine remaining useful life prediction

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