Estimating Remaining Useful Life Using Actuarial Methods
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
In many instances, condition monitoring equipment has not been installed on machinery. Yet, operators still need guidance as to when to perform maintenance that is better than what is offered by the equipment manufacturers. For these systems, running hours, counts, or some other measure of usage may be available. This data, along with failure rate data, can provide an expected time to failure, and the estimated remaining useful life. The failure rate (even small sample size) is used to estimate the shape and scale parameters for the Weibull distribution. Then the conditional expectation of the truncated survival function of the Weibull is used to estimate the time to failure. This is an actuarial technique to solve the conditional survival function problem of: given that the equipment has survived to time x, what is that probability of the equipment surviving to time x + y. The inverse cumulative distribution of the truncated survival function can then be used to estimate the remaining useful life, that is: a time when the conditional likelihood of failure is small, such as 10%. The 90% confidence of the shape and scale parameters is then used to give a bound on the remaining useful life. This method is then tested on a real world bearing dataset.
How to Cite
##plugins.themes.bootstrap3.article.details##
Weibull distribution, Conditional Probability, RUL, survival function
Bechhoefer, E., He, D. (2012). A Process for Data Driven Prognostics. MFPT Proceeding.
Cohen, A. (1965). Maximum Likelihood Estimation in the Weibull Distribution based on Complete and Censored Samples. Technometrics, Vol. 7, No 4, 1965.
London, D., (1997). Survival Models and Their Estimation. ACTEX Publishing Winsted, CT.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.