Probabilistic Models for Monitoring and Fault Diagnosis

Vandi Verma, Joquin Fernandez, and Reid Simmons
The Second IARP and IEEE/RAS Joint Workshop on Technical Challenges for Dependable Robots in Human Environments., October, 2002


Download
Not available for download due to copyright restrictions. Please contact the author(s) for a copy.

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract
Reliably detecting and diagnosing faults is very important for autonomous systems. The problem is made difficult due to the large number of faults that can occur and the fact that most faults cannot be observed directly, but must be inferred from noisy sensor readings. Probabilistic models, such as Partially Observable Markov Decision Processes (POMDPs), are a natural representation for tracking the state of a stochastic system. To be useful for fault diagnosis, however, these models must be able to perform in real time and should be able to account for both anticipated and unanticipated faults. This paper presents some of our ongoing work in using POMDPs and particle filters for modeling and tracking faults in autonomous systems. We demonstrate how these methods can be used to detect, diagnose, and recover from faults, operating in real time on-board mobile robots.

Keywords
Fault Diagnosis, POMDP, Particle Filter, Fault Detection, Fault Identification

Notes

Text Reference
Vandi Verma, Joquin Fernandez, and Reid Simmons, "Probabilistic Models for Monitoring and Fault Diagnosis," The Second IARP and IEEE/RAS Joint Workshop on Technical Challenges for Dependable Robots in Human Environments., October, 2002

BibTeX Reference
@incollection{Verma_2002_4107,
   author = "Vandi Verma and Joquin Fernandez and Reid Simmons",
   editor = "Raja Chatila",
   title = "Probabilistic Models for Monitoring and Fault Diagnosis",
   booktitle = "The Second IARP and IEEE/RAS Joint Workshop on Technical Challenges for Dependable Robots in Human Environments.",
   address = "Toulouse, France",
   month = "October",
   year = "2002",
}