Sensor data validation for nuclear power plants through Bayesian conditioning and Dempster's rule of combination

Sensor data fusion and interpretation, sensor failure detection, isolation and identification are extremely important activities for the safety of a nuclear power plant. In particular, they become critical in case of conflicts among the data. If the monitored system's description model is correct and its components work properly, then incompatibilities among data may only be attributed to temporary deterioration or permanent breakage of one or more sensors. This paper introduces and discusses three simple ideas: 1. classical model-based diagnosis can be extended straightforwardly to encompass the sensors' models into the system's description in order to diagnose even their own faults 2. from the log-file of the diagnosed minimal conflicts among the sensors, one can draw interesting conclusion regarding their relative reliability (e.g., through Bayesian conditioning) 3. the estimated reliability of the sensors is useful when assessing (e.g., through Dempster's Rule of Combination) the actual state of the monitored physical system, even in cases of conflicting data. These ideas lead to the conception of a distributed monitoring system able to attach to each sensor a statistically evaluated relative reliability, which is especially useful for devices situated in dangerous zones or areas, difficult to reach inside huge and complex power plants.