Residual life prediction under condition monitoring
Shu Jie Liu1, Ya Wei Hu1, Chao Li1, Hong Chao Zhang1, 2
COMPUTER MODELLING & NEW TECHNOLOGIES 2014 18(5) 55-60
1School of Mechanical Engineering, Dalian University of Technology, Dalian, China
2Dept. of Industrial Engineering, Texas Tech University, Texas, America
Reliability assessment and remaining life prediction in the working processes of mechanical products, getting more attention of researchers, can reduce accidents and losses and help improve the preventive maintenance decision-making. This article presents two failure models, linear and exponential, to predict residual life distribution based on the degradation information of mechanical products. Parameters of the models can be estimated using maximum likelihood method. After the real-time monitoring information is acquired, residual life distribution should be updated constantly in order to improve accuracy of the prediction. Experiments were carried out on a double row cylindrical roller bearing to get the vibration information. It proved the validity of the aforementioned method and was applied to compare the two degradation models.