TY - GEN
T1 - Comparison study on general methods for modeling lifetime data with covariates
AU - Liao, Haitao
AU - Karimi, Samira
N1 - Funding Information:
This work is sponsored by the U.S. National Science Foundation under grant CMMI-1635379.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/10/20
Y1 - 2017/10/20
N2 - Lifetime data with covariates (e.g., temperature, humidity, and electric current) are frequently seen in science and engineering. An important example is accelerated life testing (ALT) data. In ALT, test units of a product are exposing to severer-than-normal conditions to expedite product failure. The resulting lifetime and/or censoring data with covariates are often modeled by a probability distribution along with a life-stress relationship. However, if the probability distribution and the life-stress relationship selected cannot adequately describe the underlying failure process, the resulting reliability prediction will be misleading. This paper develops a new method for modeling lifetime data with covariates using phase-type (PH) distributions and a general life-stress relationship formulation. A numerical study is presented to compare the performance of this method with a mixture of Weibull distributions model. This general method creates a new direction for modeling and analyzing lifetime data with covariates for situations where the data-generating mechanisms are unknown or difficult to analyze using existing parametric ALT models and statistical tools.
AB - Lifetime data with covariates (e.g., temperature, humidity, and electric current) are frequently seen in science and engineering. An important example is accelerated life testing (ALT) data. In ALT, test units of a product are exposing to severer-than-normal conditions to expedite product failure. The resulting lifetime and/or censoring data with covariates are often modeled by a probability distribution along with a life-stress relationship. However, if the probability distribution and the life-stress relationship selected cannot adequately describe the underlying failure process, the resulting reliability prediction will be misleading. This paper develops a new method for modeling lifetime data with covariates using phase-type (PH) distributions and a general life-stress relationship formulation. A numerical study is presented to compare the performance of this method with a mixture of Weibull distributions model. This general method creates a new direction for modeling and analyzing lifetime data with covariates for situations where the data-generating mechanisms are unknown or difficult to analyze using existing parametric ALT models and statistical tools.
KW - accelerated life testing
KW - phase-type distributions
UR - http://www.scopus.com/inward/record.url?scp=85039958002&partnerID=8YFLogxK
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U2 - 10.1109/PHM.2017.8079122
DO - 10.1109/PHM.2017.8079122
M3 - Conference contribution
AN - SCOPUS:85039958002
T3 - 2017 Prognostics and System Health Management Conference, PHM-Harbin 2017 - Proceedings
BT - 2017 Prognostics and System Health Management Conference, PHM-Harbin 2017 - Proceedings
A2 - Liu, Datong
A2 - Wang, Shaojun
A2 - Liao, Haitao
A2 - Zhang, Bin
A2 - Miao, Qiang
A2 - Peng, Yu
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE Prognostics and System Health Management Conference, PHM-Harbin 2017
Y2 - 9 July 2017 through 12 July 2017
ER -