A Stochastic Methodology for Prognostics Under Time-Varying Environmental Future Profiles

author: Linkan Bian, Systems Monitoring and Prognostics (SMP) Laboratory, Georgia Institute of Technology
produced by: NASA Ames Video and Graphics Branch
published: June 27, 2012,   recorded: October 2011,   views: 3584
Categories

See Also:

Download slides icon Download slides: cidu2011_bian_stochastic_01.pdf (654.6┬áKB)


Help icon Streaming Video Help

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

We present a stochastic model of a sensor-based degradation signal for predicting, in real time, the residual lifetime of individual components subjected to a time-varying environment. We consider future environmental profi les that evolve in a deterministic manner. Unique to our model is the union of historical data with real time sensor-based data to update the degradation model and the residual life distribution (RLD) of the component within a Bayesian framework. The performance of our model is evaluated based on degradation signals from both numerical experiments and a case study using real bearing data. The results show that our approach provides more accurate estimates of the RLD, compared with benchmark models.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: