A remaining useful life prediction method for bearing based on deep neural networks

Measurement ◽  
2021 ◽  
Vol 172 ◽  
pp. 108878
Author(s):  
Hua Ding ◽  
Liangliang Yang ◽  
Zeyin Cheng ◽  
Zhaojian Yang
Keyword(s):  
Neural Networks ◽  
Life Prediction ◽  
Deep Neural Networks ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Useful Life

A Two-Stage Approach for the Remaining Useful Life Prediction of Bearings Using Deep Neural Networks

2019 ◽  
Vol 15 (6) ◽  
pp. 3703-3711 ◽  
Author(s):  
Min Xia ◽  
Teng Li ◽  
Tongxin Shu ◽  
Jiafu Wan ◽  
Clarence W. de Silva ◽  
...  
Keyword(s):  
Neural Networks ◽  
Life Prediction ◽  
Deep Neural Networks ◽  
Remaining Useful Life ◽  
Two Stage ◽  
Useful Life

Hybrid remaining useful life prediction method. A case study on railway D-cables

2021 ◽  
pp. 107746
Author(s):  
Yu Zang ◽  
Wei Shangguan ◽  
Baigen Cai ◽  
Huasheng Wang ◽  
Michael. G. Pecht
Keyword(s):  
Life Prediction ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Useful Life

Remaining useful life prediction method for machine tools based on meta-action theory

2021 ◽  
pp. 1748006X2110025
Author(s):  
Zongyi Mu ◽  
Yan Ran ◽  
Genbao Zhang ◽  
Hongwei Wang ◽  
Xin Yang
Keyword(s):  
Machine Tools ◽  
Life Prediction ◽  
Action Theory ◽  
Prediction Method ◽  
Operational Reliability ◽  
Remaining Useful Life ◽  
Practical Application ◽  
Dynamic Prediction ◽  
Degradation Model ◽  
Useful Life

Remaining useful life (RUL) is a crucial indictor to measure the performance degradation of machine tools. It directly affects the accuracy of maintenance decision-making, thus affecting operational reliability of machine tools. Currently, most RUL prediction methods are for the parts. However, due to the interaction among the parts, even RUL of all the parts cannot reflect the real RUL of the whole machine. Therefore, an RUL prediction method for the whole machine is needed. To predict RUL of the whole machine, this paper proposes an RUL prediction method with dynamic prediction objects based on meta-action theory. Firstly, machine tools are decomposed into the meta-action unit chains (MUCs) to obtain suitable prediction objects. Secondly, the machining precision unqualified rate (MPUR) control chart is used to conduct an out of control early warning for machine tools’ performance. At last, the Markov model is introduced to determine the prediction objects in next prediction and the Wiener degradation model is established to predict RUL of machine tools. According to the practical application, feasibility and effectiveness of the method is proved.

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