Skip to main content
Article thumbnail
Location of Repository

Any time probabilistic sensor validation

By PH Ibarguengoytia

Abstract

Many applications of computing, such as those in medicine and the control of manufacturing and power plants, utilize sensors to obtain information. Unfortunately, sensors are prone to failures. Even with the most sophisticated instruments and control systems, a decision based on faulty data could lead to disaster. This thesis develops a new approach to sensor validation. The thesis proposes a layered approach to the use of sensor information where the lowest layer validates sensors and provides information to the higher layers that model the process. The approach begins with a Bayesian network that defines the dependencies between the sensors in the process. Probabilistic propagation is used to estimate the value of a sensor based on its related sensors. If this estimated value differs from the actual value, then a potential fault is detected. The fault is only potential since it may be that the estimated value was based on a faulty reading. This process can be repeated for all the sensors resulting in a set of potentially faulty sensors. The real faults are isolated from the apparent ones by using a lemma whose proof is based on the properties of a Markov blanket. In order to perform in a real time environment, an any time version of the algorithm has been developed. That is, the quality of the answer returned by the algorithm improves continuously with time. The approach is compared and contrasted with other methods of sensor validation and an empirical evaluation of the sensor validation algorithm is carried out. The empirical evaluation presents the results obtained when the algorithm is applied to the validation of temperature sensors in a gas turbine of a power plant

Topics: QA75, QA, other
OAI identifier: oai:usir.salford.ac.uk:2061

Suggested articles

Citations

  1. (1992). A bayesian method for the induction of probabilistic networks from data.',
  2. (1992). A fully integrated real time multi tasking knowledge based system: application to an on board diagnostic system,
  3. (1997). A layered, any time approach to sensor validation,
  4. (1991). A logic and time nets for probabilistic inference,
  5. (1994). A structured view of real time problem solving',
  6. (1976). A survey of design methods for fault detection in dynamics systems',
  7. (1988). An analysis of time dependent planning,
  8. (1995). Anytime in diagrams, in `IJCAI-95 Workshop on Anytime Algorithms and Deliberation Scheduling',
  9. (1992). Application of neural networks for sensor validation and plant monitoring', Nuclear Technology.
  10. (1968). Approximating discrete probability distributions with dependence trees.',
  11. (1994). Arti intelligence,
  12. (1992). Automated decision analytic diagnosis of thermal performance in gas turbines,
  13. (1989). Bounded conditioning: Flexible inference for decisions under scarce resources,
  14. (1993). Circa: A cooperative intelligent real time control architecture',
  15. (1990). Conditional independence and its representation, in
  16. (1990). Constructor: a system for induction of probabilistic models,
  17. (1988). Detecting changes in signals and systems',
  18. (1995). Display of information for time-critical decision making,
  19. (1995). Empirical methods for arti intelligence, MIT press,
  20. (1974). Human and computer aided diagnosis of abdominal pain: Further report with enphasis on performance',
  21. (1993). Hyper Markov laws in the statistical analysis of decomposable graphical models',
  22. (1989). Identifying independence in bayesian networks,
  23. (1993). Incremental probabilistic inference, in `Proceedings of the Ninth Conference on Uncertainty in Arti Intelligence',
  24. (1995). Induction of dependence structures from data and its application to ozone prediction,
  25. (1951). Information and suciency',
  26. (1996). Learning Bayesian networks with local structure,
  27. (1994). Learning Bayesian networks: The combination of knowledge and statistical data,
  28. (1989). Local sensor validation',
  29. (1985). Measurement uncertainty,
  30. (1988). Misconceptions about real time computing: a serious problem for next generation systems',
  31. (1988). On the logic of causal models,
  32. (1993). Operational rationality through compilation of Any time algorithms,
  33. (1994). Operations for learning with graphical models',
  34. (1996). Optimal composition of real-time systems', doi
  35. (1988). Probabilistic reasoning in intelligent systems: networks of plausible inference,
  36. (1990). Probabilistic resoning in expert systems,
  37. (1988). Probabilistic temporal reasoning,
  38. (1988). Real time knowledge based systems',
  39. (1993). Real time sensor data validation,
  40. (1996). Real time sensor validation with probabilistic reasoning,
  41. (1987). Reasoning about beliefs and actions under computational resource constraints,
  42. (1995). Reasoning about noisy sensors in the situation calculus,
  43. (1991). Reducing problem solving variance to improve predictability',
  44. (1988). Representing and computing temporally scoped beliefs,
  45. (1990). Representing time in causal probabilistic networks,
  46. (1996). Robust distributed computing and sensing algorithm',
  47. (1995). Sensor validation and
  48. (1992). Sensor validation in power plants using neural networks,
  49. (1991). Supervised and unsupervised discretization of continuous features,
  50. (1983). Techniques for sensor based diagnosis, in
  51. (1995). The challenges of real time ai',
  52. (1990). The computational complexity of probabilistic inference using bayesian networks',
  53. (1949). The mathematical theory of communication,
  54. (1993). The self-validating sensor: rationale, de and examples',
  55. (1996). TIGER: knowledge based gas turbine condition monitoring',
  56. (1991). Time dependent utility and action under uncertainty,
  57. (1997). treenets: A framework for anytime evaluation of belief networks,
  58. (1992). Value-driven real time diagnosis, in `Proceedings of the Third International workshop on the principles of diagnosis'.

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