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Research Papers

Two-Zone Proportional Hazard Model for Equipment Remaining Useful Life Prediction

[+] Author and Article Information
Ming-Yi You

State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, No. 800, Dong Chuan Road, Shanghai 200240, Chinayoumingyi@sjtu.edu.cn

Lin Li1

Department of Mechanical Engineering, University of Michigan–Ann Arbor, 1035 H. H. Dow, 2300 Hayward Street, Ann Arbor, MI 48109-2136lilz@umich.edu

Guang Meng

State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, No. 800, Dong Chuan Road, Shanghai 200240, Chinagmeng@sjtu.edu.cn

Jun Ni

Department of Mechanical Engineering, University of Michigan–Ann Arbor, 1023 H. H. Dow, 2300 Hayward Street, Ann Arbor, MI 48109-2136junni@umich.edu

1

Corresponding author.

J. Manuf. Sci. Eng 132(4), 041008 (Jul 22, 2010) (6 pages) doi:10.1115/1.4001580 History: Received April 13, 2009; Revised April 05, 2010; Published July 22, 2010; Online July 22, 2010

Since pioneering work in 1972, the proportional hazard model (PHM) has been widely studied for survival analysis in the area of medicine. Recently, applying the PHM in the area of reliability engineering attracts significant research attentions. In this paper, a two-zone PHM is investigated to predict equipment remaining useful life (RUL) based on the practice that the equipment lifecycle could be divided into two zones: a stable zone and a degradation zone. Results from the numerical experiment illustrate that RUL prediction by applying the proposed two-zone PHM is more accurate and reliable than prediction using the traditional PHM for the entire lifecycle. In practice, this improvement is crucial for real-time maintenance decision making to prevent equipment from catastrophic failures.

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Copyright © 2010 by American Society of Mechanical Engineers
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Figures

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Figure 1

Typical failure pattern

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Figure 2

Comparison of time interval adopted by conventional PHM and PHM for the degradation zone

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Figure 3

RMS of four units from initialization to failure

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Figure 4

PDF of AEE in threshold point estimation for all 60 units

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Figure 5

Prediction results for unit 8

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