A two-phase-based deep neural network for simultaneous health monitoring and prediction of rolling bearings

Bai R.; Li Y.; Noman K.Feng K.Peng Z.

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A two-phase-based deep neural network for simultaneous health monitoring and prediction of rolling bearings

机译：A two-phase-based deep neural network for simultaneous health monitoring and prediction of rolling bearings

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? 2023 Elsevier LtdSimultaneous health monitoring and remaining useful life (RUL) prediction are important objectives in ensuring operational reliability and efficient maintenance of rolling bearings. However, most existing methods ignore the correlation between different degradation stages and RUL, and rarely study the uncertainty quantification of prediction. To overcome these issues, this paper proposes a two-phase-based deep neural network (TPDNN) method, which enables health monitoring and RUL prediction of bearings while providing uncertainty quantification. A logarithmic squared envelope-based diversity entropy is proposed to dynamically evaluate the health status of the bearings, and different degradation stages and RUL labels are adaptively established. Then the feedforward neural network is then used to achieve degradation stage (DS) identification in the first phase. The initial RUL prediction and two kinds of uncertainty quantification are implemented through the bayesian neural network in the second phase. Eventually, the correlation of the DS identification and RUL predictions is handled using a smoothing operator to obtain the final RUL. Experiments and comparisons on two bearing datasets verified that TPDNN has satisfactory prediction performance.

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《Reliability engineering & system safety》 |2023年第10期|1.1-1.16|共16页
作者
Bai R.; Li Y.; Noman K.Feng K.Peng Z.;
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作者单位

School of Aeronautics Northwestern Polytechnical University;

School of Civil Aviation Northwestern Polytechnical University;

Department of Industrial Systems Engineering and Management National University of SingaporeSchool of Mechanical Engineering Ningxia University;

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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
中图分类安全科学;
关键词
Bayesian neural network; Health monitoring; Remaining useful life prediction; Rolling bearings; Uncertainty quantification;

A two-phase-based deep neural network for simultaneous health monitoring and prediction of rolling bearings

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