Any Time Probabilistic Reasoning for Sensor Validation
This work addresses sensor validation for real-time systems like power plants, but it is incremental as it builds on existing Bayesian network methods.
The paper tackles the problem of validating sensor data in real-time applications by introducing an any-time probabilistic algorithm that uses two Bayesian network models to identify and isolate faulty sensors, achieving results that provide probabilities of sensor correctness and overall quality at each step.
For many real time applications, it is important to validate the information received from the sensors before entering higher levels of reasoning. This paper presents an any time probabilistic algorithm for validating the information provided by sensors. The system consists of two Bayesian network models. The first one is a model of the dependencies between sensors and it is used to validate each sensor. It provides a list of potentially faulty sensors. To isolate the real faults, a second Bayesian network is used, which relates the potential faults with the real faults. This second model is also used to make the validation algorithm any time, by validating first the sensors that provide more information. To select the next sensor to validate, and measure the quality of the results at each stage, an entropy function is used. This function captures in a single quantity both the certainty and specificity measures of any time algorithms. Together, both models constitute a mechanism for validating sensors in an any time fashion, providing at each step the probability of correct/faulty for each sensor, and the total quality of the results. The algorithm has been tested in the validation of temperature sensors of a power plant.