Investigation of Features for Ball Bearings Remaining Useful Life Prediction

2021 ◽  
Author(s):  
Fatemeh Hosseinpour ◽  
Enrico Zio ◽  
Mehdi Behzad
Keyword(s):  
Life Prediction ◽  
Remaining Useful Life ◽  
Ball Bearings ◽  
Useful Life

scholarly journals Remaining useful life prediction for ball bearings based on health indicators

2019 ◽  
Vol 261 ◽  
pp. 02003
Author(s):  
Zeina Al Masry ◽  
Patrick Schaible ◽  
Noureddine Zerhouni ◽  
Christophe Varnier
Keyword(s):  
Probabilistic Model ◽  
Life Prediction ◽  
Simulated Data ◽  
Health Indicators ◽  
Remaining Useful Life ◽  
Prognostic Models ◽  
Ball Bearings ◽  
Scientific Problem ◽  
Useful Life

Uncertainty in remaining useful life (RUL) prediction is nowadays a scientific problem that occupies industrials. Many prognostic models have been developed to respond to this issue from probabilistic to non-probabilistic approaches. In this paper, we deal with a non- probabilistic model for RUL prediction. For this purpose, we propose a model, which is based on health indicators information, that allows to estimate the RUL of ball bearings. The method is applied to simulated data provided by the PRONOSTIA platform designed and realized at AS2M department of FEMTO- ST Institute.

Remaining useful life prediction of ball-bearings based on high-frequency vibration features

2017 ◽  
Vol 232 (18) ◽  
pp. 3224-3234 ◽  
Author(s):  
Mehdi Behzad ◽  
Hesam Addin Arghand ◽  
Abbas Rohani Bastami
Keyword(s):  
High Frequency ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Accurate Estimation ◽  
Ball Bearings ◽  
Feed Forward Neural Network ◽  
High Frequency Vibration ◽  
Useful Life ◽  
New Feature ◽  
The Time Domain

Selecting appropriate features from the vibration condition monitoring data of ball-bearings is one of the main challenges in the application of data-driven methods for remaining useful life prediction purpose. In this article, a new feature based on the high-frequency vibration of ball-bearings is proposed. The feed forward neural network will be used for training and prediction. The experimental data of the bearing accelerated life in the PROGNOSTIA test (published in PHM 2012 IEEE conference) are used to verify the method. The results obtained by applying new features are compared with those of two popular features in the time domain (RMS and kurtosis) for prognostic purpose. Applying the proposed feature shows more accurate estimation of the bearings’ remaining useful life.

An online transfer learning-based remaining useful life prediction method of ball bearings

Measurement ◽  
2021 ◽  
Vol 176 ◽  
pp. 109201 ◽  
Author(s):  
Fuchuan Zeng ◽  
Yiming Li ◽  
Yuhang Jiang ◽  
Guiqiu Song
Keyword(s):  
Transfer Learning ◽  
Life Prediction ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Ball Bearings ◽  
Useful Life

Dynamic Modeling and Wear-Based Remaining Useful Life Prediction of High Power Clutch Systems

2005 ◽  
Vol 48 (2) ◽  
pp. 208-217 ◽  
Author(s):  
Matthew Watson ◽  
Carl Byington ◽  
Douglas Edwards ◽  
Sanket Amin
Keyword(s):  
Dynamic Modeling ◽  
High Power ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Useful Life

scholarly journals A Fusion Prognostic Method for Remaining Useful Life Prediction Based on an Extended Belief Rule Base and Particle Filters

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Bincheng Wen ◽  
Mingqing Xiao ◽  
Guanghao Wang ◽  
Zhao Yang ◽  
Jianfeng Li ◽  
...  
Keyword(s):  
Life Prediction ◽  
Particle Filters ◽  
Remaining Useful Life ◽  
Rule Base ◽  
Belief Rule Base ◽  
Useful Life
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