Skip to main content
Article thumbnail
Location of Repository

A Markov Chain state transition approach to establishing critical phases for AUV reliability

By Mario Brito and Gwyn Griffiths

Abstract

The deployment of complex autonomous underwater platforms for marine science comprises a series of sequential steps. Each step is critical to the success of the mission. In this paper we present a state transition approach, in the form of a Markov chain, which models the sequence of steps from pre-launch to operation to recovery. The aim is to identify the states and state transitions that present higher risk to the vehicle and hence to the mission, based on evidence and judgment. Developing a Markov chain consists of two separate tasks. The first defines the structure that encodes the sequence of events. The second task assigns probabilities to each possible transition. Our model comprises eleven discrete states, and includes distance-dependent underway survival statistics. The integration of the Markov model with underway survival statistics allows us to quantify the likelihood of success during each state and transition and consequently the likelihood of achieving the desired mission goals. To illustrate this generic process, the fault history of the Autosub3 autonomous underwater vehicle provides the information for different phases of operation. The method proposed here adds more detail to previous analyses; faults are discriminated according to the phase of the mission in which they took place

Topics: TC
Year: 2011
OAI identifier: oai:eprints.soton.ac.uk:69179
Provided by: e-Prints Soton

Suggested articles

Citations

  1. (In prep.) An extension to the Kaplan Meier nonparametric estimator when death is not inevitable. In preparation.
  2. (2009). A Bayesian Approach to Predicting Risk of Loss During Autonomous Underwater Vehicle Missions. doi
  3. (1994). A Markov Chain Model for Statistical Software Testing. doi
  4. (1950). An Introduction to Probability Theory and its Applications, doi
  5. (1999). Combining Probability Distributions from Experts in Risk Analysis. Risk Analysis, doi
  6. (2008). Eliciting expert judgment on the probability of loss of an AUV operating in four environments.
  7. (2000). Getting Autosub back.
  8. (2007). Insurance for autonomous underwater vehicles. doi
  9. (2009). Mathworks:Matlab version 12. Available online: http://www.mathworks.com/products/matlab/.
  10. (1958). Nonparametric estimation from incomplete observations. doi
  11. (2003). Office of the Secretary of Defense, doi
  12. (1970). On determining the reliability of protective relay systems. doi
  13. (2003). On the reliability of the Autosub autonomous underwater vehicle. doi
  14. (2009). Predicting Future Excess Events in Risk Assessment. Risk analysis, doi
  15. (2008). Predicting risk in missions under sea ice with Autonomous Underwater Vehicles. In, doi
  16. (2009). Quantitative Assessment of Building Fire Risk to Life Safety. doi
  17. (1986). Quantitative Reliability Evaluation of Repairable PhasedMission Systems Using Markov Approach. doi
  18. (1997). Reliability estimation of semi-Markov systems: a case study. doi
  19. (2004). Reliability growth of autonomous underwater vehicle Dorado. In: doi
  20. (2006). Report of the inquiry into the loss of
  21. (2009). Results of Expert Judgments on the Faults and Risks with Autosub3 and an Analysis of its Campaign to Pine Island Bay, Antarctica,
  22. (2007). Towards a risk management process for autonomous underwater vehicles.
  23. (2003). Uncertain Judgments: Eliciting Experts’ Probabilities. doi
  24. (2008). Using Expert Judgments on Autonomous Underwater Vehicle Probability of Loss to Estimate Vehicle Survivability in Four Operating Environments. Risk Analysis, submitted for publication.

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.