Bayesian information fusion method for reliability analysis with failure‐time data and degradation data

2022 ◽  
Author(s):  
Junyu Guo ◽  
Yan‐Feng Li ◽  
Weiwen Peng ◽  
Hong‐Zhong Huang
Keyword(s):  
Reliability Analysis ◽  
Information Fusion ◽  
Failure Time ◽  
Failure Time Data ◽  
Fusion Method ◽  
Time Data ◽  
Degradation Data

scholarly journals Remaining Useful Life Prediction With Fusing Failure Time Data and Field Degradation Data With Random Effects

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 11964-11978 ◽  
Author(s):  
Shengjin Tang ◽  
Xiaodong Xu ◽  
Chuanqiang Yu ◽  
Xiaoyan Sun ◽  
Hongdong Fan ◽  
...  
Keyword(s):  
Random Effects ◽  
Life Prediction ◽  
Failure Time ◽  
Remaining Useful Life ◽  
Failure Time Data ◽  
Time Data ◽  
Degradation Data ◽  
Useful Life

scholarly journals Reliability Analysis of Repairable Systems With Incomplete Failure Time Data

2018 ◽  
Vol 67 (3) ◽  
pp. 1043-1059 ◽  
Author(s):  
Wujun Si ◽  
Qingyu Yang ◽  
Leslie Monplaisir ◽  
Yong Chen
Keyword(s):  
Reliability Analysis ◽  
Failure Time ◽  
Failure Time Data ◽  
Repairable Systems ◽  
Time Data

A unified approach to variable selection for Cox’s proportional hazards model with interval-censored failure time data

2021 ◽  
pp. 096228022110092
Author(s):  
Mingyue Du ◽  
Hui Zhao ◽  
Jianguo Sun
Keyword(s):  
Variable Selection ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Failure Time ◽  
Significant Feature ◽  
Failure Time Data ◽  
Time Data ◽  
Hazards Model ◽  
Interval Censored ◽  
Cox's Proportional Hazards Model

Cox’s proportional hazards model is the most commonly used model for regression analysis of failure time data and some methods have been developed for its variable selection under different situations. In this paper, we consider a general type of failure time data, case K interval-censored data, that include all of other types discussed as special cases, and propose a unified penalized variable selection procedure. In addition to its generality, another significant feature of the proposed approach is that unlike all of the existing variable selection methods for failure time data, the proposed approach allows dependent censoring, which can occur quite often and could lead to biased or misleading conclusions if not taken into account. For the implementation, a coordinate descent algorithm is developed and the oracle property of the proposed method is established. The numerical studies indicate that the proposed approach works well for practical situations and it is applied to a set of real data arising from Alzheimer’s Disease Neuroimaging Initiative study that motivated this study.

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