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Research Papers

Real-Time Estimation of Mean Remaining Life Using Sensor-Based Degradation Models

[+] Author and Article Information
Alaa Elwany

 Georgia Institute of Technology, 765 Ferst Drive, NW, Atlanta, GA 30313elwany@gatech.edu

Nagi Gebraeel

 Georgia Institute of Technology, 765 Ferst Drive, NW, Atlanta, GA 30313

J. Manuf. Sci. Eng 131(5), 051005 (Sep 08, 2009) (9 pages) doi:10.1115/1.3159045 History: Received July 09, 2008; Revised May 05, 2009; Published September 08, 2009

Advances in sensor technology have led to an increased interest in using degradation-based sensory information to predict the remaining lives of partially degraded components and systems. This paper presents a stochastic degradation modeling framework for computing and continuously updating remaining life distributions (RLDs) using in situ degradation signals acquired from individual components during their operational lives. Unfortunately, these sensory-updated RLDs cannot be characterized using parametric distributions and their moments do not exist. Such difficulties hinder the implementation of this sensor-based framework, especially from the standpoint of computational efficiency of embedded algorithms. In this paper, we identify an approximate procedure by which we can compute a conservative mean of the sensory-updated RLDs and express the mean and variance using closed-form expressions that are easy to evaluate. To accomplish this, we use the first passage time of Brownian motion with positive drift, which follows an inverse Gaussian distribution, as an approximation of the remaining life. We then show that the mean of the inverse Gaussian is a conservative lower bound of the mean remaining life using Jensen’s inequality. The results are validated using real-world vibration-based degradation information.

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Copyright © 2009 by American Society of Mechanical Engineers
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Figures

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Figure 1

Setup for accelerated degradation testing

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Figure 2

Evolution of bearing vibration spectra

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Figure 3

Vibration-based degradation signal

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Figure 4

Plot of the computed error increments

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Figure 5

Updated CDFs at different degradation percentiles for bearing 50

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Figure 6

Updated PDFs at different degradation percentiles for bearing 50

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Figure 7

Proposed methodology—95% CI for prediction errors based on the mean

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Figure 8

Proposed methodology—95% CI for prediction errors based on the median

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Figure 9

Policy 1—95% CI of prediction errors based on the median

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Figure 10

Policy 2—95% CI for prediction errors based on the median

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